diff --git a/.gitignore b/.gitignore index 6769e21..1c54923 100644 --- a/.gitignore +++ b/.gitignore @@ -157,4 +157,7 @@ cython_debug/ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ \ No newline at end of file +#.idea/ + +# Experiments results +ray_results/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..e6161f5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,10 @@ +FROM rayproject/ray:2.38.0-py312-gpu + +RUN sudo apt update && sudo apt install -y libgdal-dev gdal-bin + +WORKDIR /app + +COPY pyproject.toml ./ +RUN pip install --no-cache-dir . && pip uninstall -y app + +CMD ["/bin/sh"] \ No newline at end of file diff --git a/README.md b/README.md index 72b7fe7..b93f740 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,42 @@ Welcome to the official GitHub repository for the Drone Swarm Search (DSSE) algo Explore a diverse range of implementations that leverage the latest advancements in machine learning to solve complex coordination tasks in dynamic and unpredictable environments. +## How to run + +Arguments inside [brackets] are optional. + +#### Run a training script + +```sh +python run_script --file [--exp_name ] [--n_agents ] +``` + +Example: training a MLP on the coverage environment. +```sh +python run_script --file train_ppo_mlp_cov.py --exp_name training_mlp_cov +``` + + +#### Run a test script + +```sh +python run_script.py --file --checkpoint [--matrix_path ] [--see] [--n_agents ] +``` + +The --see switch makes shows you the agents playing and records a GIF on the env instead of collecting metrics. + +Example: Evaluating 4 agents trained on coverage env +```sh +python run_script.py --file test_trained_cov_mlp.py --checkpoint --matrix_path data/min_matrix.npy --n_agents 4 +``` + + +#### Using the docker container + +``` +docker compose run --rm dsse-algorithms +``` + ## 📚 Documentation Links - **[Documentation Site](https://pfeinsper.github.io/drone-swarm-search/)**: Access detailed tutorials, usage examples, and comprehensive technical documentation. This resource is essential for understanding the DSSE framework and integrating these algorithms into your projects effectively. diff --git a/src/min_matrix.npy b/data/min_matrix.npy similarity index 100% rename from src/min_matrix.npy rename to data/min_matrix.npy diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..6d51ca9 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,19 @@ +version: '3.9' + +services: + dsse-algorithms: + image: dsse-algorithms + build: . + container_name: dsse-algorithms + working_dir: /app + volumes: + - .:/app # Mounts your current folder + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + stdin_open: true + command: /bin/sh \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..3f35699 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,92 @@ +[project] +name = "drone-swarm-search-algorithms" +version = "0.1.0" +description = "Algorithms to solve the DSSE environment, focusing on optimizing drone swarm search and navigation for critical applications." +authors = [ + { name="Ricardo Ribeiro Rodrigues", email="ricardorr7@al.insper.edu.br" }, + { name="Renato Laffranchi Falcão", email="renatolf1@al.insper.edu.br" }, + { name="Pedro Henrique Britto Aragão Andrade", email="pedroa3@al.insper.edu.br" }, + { name="Jorás Oliveira", email="jorascco@al.insper.edu.br" }, + { name="Fabricio Barth", email="fabriciojb@insper.edu.br" }, +] +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "aiosignal==1.3.1", + "attrs==24.2.0", + "certifi==2024.8.30", + "charset-normalizer==3.3.2", + "click==8.1.7", + "cloudpickle==3.0.0", + "contourpy==1.3.0", + "cycler==0.12.1", + "dm-tree==0.1.8", + "dsse[coverage]==1.1.9", + "farama-notifications==0.0.4", + "filelock==3.16.1", + "fonttools==4.54.1", + "frozenlist==1.4.1", + "fsspec==2024.9.0", + "gymnasium==0.28.1", + "idna==3.10", + "imageio==2.35.1", + "jax-jumpy==1.0.0", + "jinja2==3.1.4", + "jsonschema-specifications==2023.12.1", + "jsonschema==4.23.0", + "kiwisolver==1.4.7", + "lazy-loader==0.4", + "llvmlite==0.43.0", + "lz4==4.3.3", + "markdown-it-py==3.0.0", + "markupsafe==2.1.5", + "matplotlib==3.8.4", + "mdurl==0.1.2", + "mpmath==1.3.0", + "msgpack==1.1.0", + "networkx==3.3", + "numba==0.60.0", + "numpy==2.0.2", + "nvidia-cublas-cu12==12.1.3.1", + "nvidia-cuda-cupti-cu12==12.1.105", + "nvidia-cuda-nvrtc-cu12==12.1.105", + "nvidia-cuda-runtime-cu12==12.1.105", + "nvidia-cudnn-cu12==9.1.0.70", + "nvidia-cufft-cu12==11.0.2.54", + "nvidia-curand-cu12==10.3.2.106", + "nvidia-cusolver-cu12==11.4.5.107", + "nvidia-cusparse-cu12==12.1.0.106", + "nvidia-nccl-cu12==2.20.5", + "nvidia-nvjitlink-cu12==12.6.68", + "nvidia-nvtx-cu12==12.1.105", + "packaging==24.1", + "pandas==2.2.3", + "pettingzoo==1.24.3", + "pillow==10.4.0", + "protobuf==5.28.2", + "pyarrow==17.0.0", + "pygame==2.6.1", + "pygments==2.18.0", + "pyparsing==3.1.4", + "python-dateutil==2.9.0.post0", + "pytz==2024.2", + "pyyaml==6.0.2", + "ray==2.38.0", + "referencing==0.35.1", + "requests==2.32.3", + "rich==13.8.1", + "rpds-py==0.20.0", + "scikit-image==0.24.0", + "scipy==1.14.1", + "shellingham==1.5.4", + "six==1.16.0", + "sympy==1.13.3", + "tensorboardx==2.6.2.2", + "tifffile==2024.9.20", + "torch==2.4.1", + "triton==3.0.0", + "typer==0.12.5", + "typing-extensions==4.12.2", + "tzdata==2024.2", + "urllib3==2.2.3", +] diff --git a/run_script.py b/run_script.py new file mode 100644 index 0000000..c207b9b --- /dev/null +++ b/run_script.py @@ -0,0 +1,68 @@ +import os +import datetime +from pathlib import Path +import importlib +import argparse + +argparser = argparse.ArgumentParser() +argparser.add_argument("--file", type=str, required=True) +argparser.add_argument("--checkpoint", type=str, required=False, default=None) +argparser.add_argument( + "--matrix_path", type=str, required=False, default=argparse.SUPPRESS +) +argparser.add_argument("--see", action="store_true", default=False) +argparser.add_argument( + "--storage_path", + type=str, + required=False, + default=f"{Path().resolve()}/ray_results/", +) +argparser.add_argument( + "--exp_name", type=str, required=False, default=str(datetime.datetime.now()) +) +argparser.add_argument("--n_agents", type=int, required=False, default=2) +argparser.add_argument("--use_random_positions", action="store_true", default=False) +args = argparser.parse_args() + + +def find_path_if_exists(file): + for root, _, files in os.walk("src"): + if file in files: + return os.path.join(root, file) + return None + + +file = args.file +path = find_path_if_exists(file) +if path is None: + print(f"File {file} not found in src") + exit(1) + +all_modules_from_src = [] +all_dirs = path.split("/") +for i, dir in enumerate(all_dirs): + if dir == "src": + break + +for dir in all_dirs[i:]: + all_modules_from_src.append(dir) + +if "test" in all_modules_from_src: + if args.checkpoint is None: + print( + "Checkpoint is required for test files, please provide it with --checkpoint" + ) + exit(1) + +if "matrix_path" not in args: + args.matrix_path = "data/min_matrix.npy" + print(f"No --matrix_path provided, using default: {args.matrix_path}") + +full_path = ".".join(all_modules_from_src[:-1]) +module_path = f"{full_path}.{Path(file).stem}" +module = importlib.import_module(module_path) + +if hasattr(module, "main"): + module.main(args) +else: + print(f"The module does not have a 'main' function.") diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/coverage/__init__.py b/src/coverage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/coverage/test/test_trained_cov.py b/src/coverage/test/test_trained_cov.py new file mode 100644 index 0000000..c3803d2 --- /dev/null +++ b/src/coverage/test/test_trained_cov.py @@ -0,0 +1,59 @@ +from DSSE import CoverageDroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +import ray +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from ray.rllib.algorithms.ppo import PPO +from src.models.ppo_cnn import PpoCnnModel +from src.models.cnn_config import CNNConfig +from src.utils.play_env import play_with_record, evaluate_agent_coverage + + +def main(args): + if args.matrix_path is None: + print("Please provide a matrix path") + exit(1) + + # Register the model + model = PpoCnnModel + model.CONFIG = CNNConfig(kernel_sizes=[(3, 3), (2, 2)]) + ModelCatalog.register_custom_model(model.NAME, model) + + def env_creator(_): + print("-------------------------- ENV CREATOR --------------------------") + N_AGENTS = 2 + env = CoverageDroneSwarmSearch( + timestep_limit=180, + drone_amount=N_AGENTS, + prob_matrix_path=args.matrix_path, + render_mode="human", + ) + env = AllPositionsWrapper(env) + grid_size = env.grid_size + positions = [ + (grid_size - 1, grid_size // 2), + (0, grid_size // 2), + ] + env = RetainDronePosWrapper(env, positions) + return env + + env = env_creator(None) + register_env( + "DSSE_Coverage", lambda config: ParallelPettingZooEnv(env_creator(config)) + ) + ray.init() + + checkpoint_path = args.checkpoint + PPOagent = PPO.from_checkpoint(checkpoint_path) + + if args.see: + play_with_record(env, PPOagent) + else: + evaluate_agent_coverage(env, PPOagent) + + env.close() + + +if __name__ == "__main__": + main(None) diff --git a/src/coverage/test/test_trained_cov_mlp.py b/src/coverage/test/test_trained_cov_mlp.py new file mode 100644 index 0000000..d72ea9c --- /dev/null +++ b/src/coverage/test/test_trained_cov_mlp.py @@ -0,0 +1,54 @@ +from DSSE import CoverageDroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllFlattenWrapper +import ray +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.tune.registry import register_env +from ray.rllib.algorithms.ppo import PPO +from src.utils.play_env import play_with_record, evaluate_agent_coverage + +def position_on_cross(grid_size, n_agents): + positions = [ + (0, grid_size // 2), + (grid_size - 1, grid_size // 2), + (grid_size // 2, 0), + (grid_size // 2, grid_size - 1), + ] + return positions[:n_agents] + + +def main(args): + def env_creator(_): + print("-------------------------- ENV CREATOR --------------------------") + render_mode = "human" if args.see else "ansi" + # 6 hours of simulation, 600 radius + env = CoverageDroneSwarmSearch( + timestep_limit=200, + drone_amount=args.n_agents, + prob_matrix_path=args.matrix_path, + render_mode=render_mode, + ) + env = AllFlattenWrapper(env) + grid_size = env.grid_size + print("Grid size: ", grid_size) + env = RetainDronePosWrapper(env, position_on_cross(grid_size, args.n_agents)) + return env + + env = env_creator(None) + register_env( + "DSSE_Coverage", lambda config: ParallelPettingZooEnv(env_creator(config)) + ) + ray.init() + + checkpoint_path = args.checkpoint + PPOagent = PPO.from_checkpoint(checkpoint_path) + + if args.see: + play_with_record(env, PPOagent) + else: + evaluate_agent_coverage(env, PPOagent) + + env.close() + + +if __name__ == "__main__": + main() diff --git a/src/coverage/train/__init__.py b/src/coverage/train/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/coverage/train/icm_learners.py b/src/coverage/train/icm_learners.py new file mode 100644 index 0000000..2ac50e4 --- /dev/null +++ b/src/coverage/train/icm_learners.py @@ -0,0 +1,164 @@ +from typing import Any, List, Optional + +import gymnasium as gym +import torch + +from ray.rllib.algorithms.dqn.torch.dqn_rainbow_torch_learner import ( + DQNRainbowTorchLearner, +) +from ray.rllib.algorithms.ppo.torch.ppo_torch_learner import PPOTorchLearner +from ray.rllib.connectors.common.add_observations_from_episodes_to_batch import ( + AddObservationsFromEpisodesToBatch, +) +from ray.rllib.connectors.common.numpy_to_tensor import NumpyToTensor +from ray.rllib.connectors.learner.add_next_observations_from_episodes_to_train_batch import ( # noqa + AddNextObservationsFromEpisodesToTrainBatch, +) +from ray.rllib.connectors.connector_v2 import ConnectorV2 +from ray.rllib.core import Columns, DEFAULT_MODULE_ID +from ray.rllib.core.learner.torch.torch_learner import TorchLearner +from ray.rllib.core.rl_module.rl_module import RLModule +from ray.rllib.utils.typing import EpisodeType + +ICM_MODULE_ID = "_intrinsic_curiosity_model" + + +class DQNTorchLearnerWithCuriosity(DQNRainbowTorchLearner): + def build(self) -> None: + super().build() + add_intrinsic_curiosity_connectors(self) + + +class PPOTorchLearnerWithCuriosity(PPOTorchLearner): + def build(self) -> None: + super().build() + add_intrinsic_curiosity_connectors(self) + + +def add_intrinsic_curiosity_connectors(torch_learner: TorchLearner) -> None: + """Adds two connector pieces to the Learner pipeline, needed for ICM training. + + - The `AddNextObservationsFromEpisodesToTrainBatch` connector makes sure the train + batch contains the NEXT_OBS for ICM's forward- and inverse dynamics net training. + - The `IntrinsicCuriosityModelConnector` piece computes intrinsic rewards from the + ICM and adds the results to the extrinsic reward of the main module's train batch. + + Args: + torch_learner: The TorchLearner, to whose Learner pipeline the two ICM connector + pieces should be added. + """ + learner_config_dict = torch_learner.config.learner_config_dict + + # Assert, we are only training one policy (RLModule) and we have the ICM + # in our MultiRLModule. + assert ( + len(torch_learner.module) == 2 + and DEFAULT_MODULE_ID in torch_learner.module + and ICM_MODULE_ID in torch_learner.module + ) + + # Make sure both curiosity loss settings are explicitly set in the + # `learner_config_dict`. + if ( + "forward_loss_weight" not in learner_config_dict + or "intrinsic_reward_coeff" not in learner_config_dict + ): + raise KeyError( + "When using the IntrinsicCuriosityTorchLearner, both `forward_loss_weight` " + " and `intrinsic_reward_coeff` must be part of your config's " + "`learner_config_dict`! Add these values through: `config.training(" + "learner_config_dict={'forward_loss_weight': .., 'intrinsic_reward_coeff': " + "..})`." + ) + + if torch_learner.config.add_default_connectors_to_learner_pipeline: + # Prepend a "add-NEXT_OBS-from-episodes-to-train-batch" connector piece + # (right after the corresponding "add-OBS-..." default piece). + torch_learner._learner_connector.insert_after( + AddObservationsFromEpisodesToBatch, + AddNextObservationsFromEpisodesToTrainBatch(), + ) + # Append the ICM connector, computing intrinsic rewards and adding these to + # the main model's extrinsic rewards. + torch_learner._learner_connector.insert_after( + NumpyToTensor, + IntrinsicCuriosityModelConnector( + intrinsic_reward_coeff=( + torch_learner.config.learner_config_dict["intrinsic_reward_coeff"] + ) + ), + ) + + +class IntrinsicCuriosityModelConnector(ConnectorV2): + """Learner ConnectorV2 piece to compute intrinsic rewards based on an ICM. + + For more details, see here: + [1] Curiosity-driven Exploration by Self-supervised Prediction + Pathak, Agrawal, Efros, and Darrell - UC Berkeley - ICML 2017. + https://arxiv.org/pdf/1705.05363.pdf + + This connector piece: + - requires two RLModules to be present in the MultiRLModule: + DEFAULT_MODULE_ID (the policy model to be trained) and ICM_MODULE_ID (the instrinsic + curiosity architecture). + - must be located toward the end of to your Learner pipeline (after the + `NumpyToTensor` piece) in order to perform a forward pass on the ICM model with the + readily compiled batch and a following forward-loss computation to get the intrinsi + rewards. + - these intrinsic rewards will then be added to the (extrinsic) rewards in the main + model's train batch. + """ + + def __init__( + self, + input_observation_space: Optional[gym.Space] = None, + input_action_space: Optional[gym.Space] = None, + *, + intrinsic_reward_coeff: float, + **kwargs, + ): + """Initializes a CountBasedCuriosity instance. + + Args: + intrinsic_reward_coeff: The weight with which to multiply the intrinsic + reward before adding it to the extrinsic rewards of the main model. + """ + super().__init__(input_observation_space, input_action_space) + + self.intrinsic_reward_coeff = intrinsic_reward_coeff + + def __call__( + self, + *, + rl_module: RLModule, + batch: Any, + episodes: List[EpisodeType], + explore: Optional[bool] = None, + shared_data: Optional[dict] = None, + **kwargs, + ) -> Any: + # Assert that the batch is ready. + assert DEFAULT_MODULE_ID in batch and ICM_MODULE_ID not in batch + assert ( + Columns.OBS in batch[DEFAULT_MODULE_ID] + and Columns.NEXT_OBS in batch[DEFAULT_MODULE_ID] + ) + # TODO (sven): We are performing two forward passes per update right now. + # Once here in the connector (w/o grad) to just get the intrinsic rewards + # and once in the learner to actually compute the ICM loss and update the ICM. + # Maybe we can save one of these, but this would currently harm the DDP-setup + # for multi-GPU training. + with torch.no_grad(): + # Perform ICM forward pass. + fwd_out = rl_module[ICM_MODULE_ID].forward_train(batch[DEFAULT_MODULE_ID]) + + # Add the intrinsic rewards to the main module's extrinsic rewards. + batch[DEFAULT_MODULE_ID][Columns.REWARDS] += ( + self.intrinsic_reward_coeff * fwd_out[Columns.INTRINSIC_REWARDS] + ) + + # Duplicate the batch such that the ICM also has data to learn on. + batch[ICM_MODULE_ID] = batch[DEFAULT_MODULE_ID] + + return batch \ No newline at end of file diff --git a/src/coverage/train/train_curiosity copy.py b/src/coverage/train/train_curiosity copy.py new file mode 100644 index 0000000..036fcfd --- /dev/null +++ b/src/coverage/train/train_curiosity copy.py @@ -0,0 +1,310 @@ +"""Example of implementing and training with an intrinsic curiosity model (ICM). + +This type of curiosity-based learning trains a simplified model of the environment +dynamics based on three networks: +1) Embedding observations into latent space ("feature" network). +2) Predicting the action, given two consecutive embedded observations +("inverse" network). +3) Predicting the next embedded obs, given an obs and action +("forward" network). + +The less the ICM is able to predict the actually observed next feature vector, +given obs and action (through the forwards network), the larger the +"intrinsic reward", which will be added to the extrinsic reward of the agent. + +Therefore, if a state transition was unexpected, the agent becomes +"curious" and will further explore this transition leading to better +exploration in sparse rewards environments. + +For more details, see here: +[1] Curiosity-driven Exploration by Self-supervised Prediction +Pathak, Agrawal, Efros, and Darrell - UC Berkeley - ICML 2017. +https://arxiv.org/pdf/1705.05363.pdf + +This example: + - demonstrates how to write a custom RLModule, representing the ICM from the paper + above. Note that this custom RLModule does not belong to any individual agent. + - demonstrates how to write a custom (PPO) TorchLearner that a) adds the ICM to its + MultiRLModule, b) trains the regular PPO Policy plus the ICM module, using the + PPO parent loss and the ICM's RLModule's own loss function. + +We use a FrozenLake (sparse reward) environment with a custom map size of 12x12 and a +hard time step limit of 22 to make it almost impossible for a non-curiosity based +learners to learn a good policy. + + +How to run this script +---------------------- +`python [script file name].py --enable-new-api-stack` + +Use the `--no-curiosity` flag to disable curiosity learning and force your policy +to be trained on the task w/o the use of intrinsic rewards. With this option, the +algorithm should NOT succeed. + +For debugging, use the following additional command line options +`--no-tune --num-env-runners=0` +which should allow you to set breakpoints anywhere in the RLlib code and +have the execution stop there for inspection and debugging. + +For logging to your WandB account, use: +`--wandb-key=[your WandB API key] --wandb-project=[some project name] +--wandb-run-name=[optional: WandB run name (within the defined project)]` + + +Results to expect +----------------- +In the console output, you can see that only a PPO policy that uses curiosity can +actually learn. + +Policy using ICM-based curiosity: ++-------------------------------+------------+-----------------+--------+ +| Trial name | status | loc | iter | +|-------------------------------+------------+-----------------+--------+ +| PPO_FrozenLake-v1_52ab2_00000 | TERMINATED | 127.0.0.1:73318 | 392 | ++-------------------------------+------------+-----------------+--------+ ++------------------+--------+----------+--------------------+ +| total time (s) | ts | reward | episode_len_mean | +|------------------+--------+----------+--------------------| +| 236.652 | 786000 | 1.0 | 22.0 | ++------------------+--------+----------+--------------------+ + +Policy NOT using curiosity: +[DOES NOT LEARN AT ALL] +""" + +from collections import defaultdict + +from ray import tune +from ray.rllib.algorithms.algorithm_config import AlgorithmConfig +from ray.rllib.algorithms.callbacks import DefaultCallbacks +from ray.rllib.connectors.env_to_module import FlattenObservations +from icm_learners import ( + ICM_MODULE_ID, + PPOTorchLearnerWithCuriosity, + DQNTorchLearnerWithCuriosity +) +from ray.rllib.core import DEFAULT_MODULE_ID +from ray.rllib.core.rl_module.multi_rl_module import MultiRLModuleSpec +from ray.rllib.core.rl_module.rl_module import RLModuleSpec +from ray.rllib.examples.rl_modules.classes.intrinsic_curiosity_model_rlm import ( + IntrinsicCuriosityModel, +) +from ray.rllib.utils.metrics import ( + ENV_RUNNER_RESULTS, + EPISODE_RETURN_MEAN, + NUM_ENV_STEPS_SAMPLED_LIFETIME, +) +from ray.rllib.utils.test_utils import ( + add_rllib_example_script_args, + run_rllib_example_script_experiment, +) + +parser = add_rllib_example_script_args( + default_iters=2000, + default_timesteps=10000000, + default_reward=0.9, +) +parser.set_defaults(enable_new_api_stack=True) + + +class MeasureMaxDistanceToStart(DefaultCallbacks): + """Callback measuring the dist of the agent to its start position in FrozenLake-v1. + + Makes the naive assumption that the start position ("S") is in the upper left + corner of the used map. + Uses the MetricsLogger to record the (euclidian) distance value. + """ + + def __init__(self): + super().__init__() + self.max_dists = defaultdict(float) + self.max_dists_lifetime = 0.0 + + def on_episode_step( + self, + *, + episode, + env_runner, + metrics_logger, + env, + env_index, + rl_module, + **kwargs, + ): + obs = episode.get_observations(-1) + num_rows = env.envs[0].unwrapped.nrow + num_cols = env.envs[0].unwrapped.ncol + row = obs // num_cols + col = obs % num_rows + curr_dist = (row**2 + col**2) ** 0.5 + if curr_dist > self.max_dists[episode.id_]: + self.max_dists[episode.id_] = curr_dist + + def on_episode_end( + self, + *, + episode, + env_runner, + metrics_logger, + env, + env_index, + rl_module, + **kwargs, + ): + # Compute current maximum distance across all running episodes + # (including the just ended one). + max_dist = max(self.max_dists.values()) + metrics_logger.log_value( + key="max_dist_travelled_across_running_episodes", + value=max_dist, + window=10, + ) + if max_dist > self.max_dists_lifetime: + self.max_dists_lifetime = max_dist + del self.max_dists[episode.id_] + + def on_sample_end( + self, + *, + env_runner, + metrics_logger, + samples, + **kwargs, + ): + metrics_logger.log_value( + key="max_dist_travelled_lifetime", + value=self.max_dists_lifetime, + window=1, + ) + + +if __name__ == "__main__": + args = parser.parse_args() + + if args.algo not in ["DQN", "PPO"]: + raise ValueError( + "Curiosity example only implemented for either DQN or PPO! See the " + ) + + base_config = ( + tune.registry.get_trainable_cls(args.algo) + .get_default_config() + .environment( + "FrozenLake-v1", + env_config={ + # Use a 12x12 map. + "desc": [ + "SFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFF", + "FFFFFFFFFFFG", + ], + "is_slippery": False, + # Limit the number of steps the agent is allowed to make in the env to + # make it almost impossible to learn without the curriculum. + "max_episode_steps": 22, + }, + ) + .callbacks(MeasureMaxDistanceToStart) + .env_runners( + num_envs_per_env_runner=5 if args.algo == "PPO" else 1, + env_to_module_connector=lambda env: FlattenObservations(), + ) + .training( + learner_config_dict={ + # Intrinsic reward coefficient. + "intrinsic_reward_coeff": 0.05, + # Forward loss weight (vs inverse dynamics loss). Total ICM loss is: + # L(total ICM) = ( + # `forward_loss_weight` * L(forward) + # + (1.0 - `forward_loss_weight`) * L(inverse_dyn) + # ) + "forward_loss_weight": 0.2, + } + ) + .rl_module( + rl_module_spec=MultiRLModuleSpec( + rl_module_specs={ + # The "main" RLModule (policy) to be trained by our algo. + DEFAULT_MODULE_ID: RLModuleSpec( + **( + {"model_config": {"vf_share_layers": True}} + if args.algo == "PPO" + else {} + ), + ), + # The intrinsic curiosity model. + ICM_MODULE_ID: RLModuleSpec( + module_class=IntrinsicCuriosityModel, + # Only create the ICM on the Learner workers, NOT on the + # EnvRunners. + learner_only=True, + # Configure the architecture of the ICM here. + model_config={ + "feature_dim": 288, + "feature_net_hiddens": (256, 256), + "feature_net_activation": "relu", + "inverse_net_hiddens": (256, 256), + "inverse_net_activation": "relu", + "forward_net_hiddens": (256, 256), + "forward_net_activation": "relu", + }, + ), + } + ), + # Use a different learning rate for training the ICM. + algorithm_config_overrides_per_module={ + ICM_MODULE_ID: AlgorithmConfig.overrides(lr=0.0005) + }, + ) + ) + + # Set PPO-specific hyper-parameters. + if args.algo == "PPO": + base_config.training( + num_epochs=6, + # Plug in the correct Learner class. + learner_class=PPOTorchLearnerWithCuriosity, + train_batch_size_per_learner=2000, + lr=0.0003, + ) + elif args.algo == "DQN": + base_config.training( + # Plug in the correct Learner class. + learner_class=DQNTorchLearnerWithCuriosity, + train_batch_size_per_learner=128, + lr=0.00075, + replay_buffer_config={ + "type": "PrioritizedEpisodeReplayBuffer", + "capacity": 500000, + "alpha": 0.6, + "beta": 0.4, + }, + # Epsilon exploration schedule for DQN. + epsilon=[[0, 1.0], [500000, 0.05]], + n_step=(3, 5), + double_q=True, + dueling=True, + ) + + success_key = f"{ENV_RUNNER_RESULTS}/max_dist_travelled_across_running_episodes" + stop = { + success_key: 8.0, + f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": args.stop_reward, + NUM_ENV_STEPS_SAMPLED_LIFETIME: args.stop_timesteps, + } + + run_rllib_example_script_experiment( + base_config, + args, + stop=stop, + success_metric={success_key: stop[success_key]}, + ) diff --git a/src/coverage/train/train_curiosity.py b/src/coverage/train/train_curiosity.py new file mode 100644 index 0000000..7c15f9f --- /dev/null +++ b/src/coverage/train/train_curiosity.py @@ -0,0 +1,219 @@ +"""Example of implementing and training with an intrinsic curiosity model (ICM). + +This type of curiosity-based learning trains a simplified model of the environment +dynamics based on three networks: +1) Embedding observations into latent space ("feature" network). +2) Predicting the action, given two consecutive embedded observations +("inverse" network). +3) Predicting the next embedded obs, given an obs and action +("forward" network). + +The less the ICM is able to predict the actually observed next feature vector, +given obs and action (through the forwards network), the larger the +"intrinsic reward", which will be added to the extrinsic reward of the agent. + +Therefore, if a state transition was unexpected, the agent becomes +"curious" and will further explore this transition leading to better +exploration in sparse rewards environments. + +For more details, see here: +[1] Curiosity-driven Exploration by Self-supervised Prediction +Pathak, Agrawal, Efros, and Darrell - UC Berkeley - ICML 2017. +https://arxiv.org/pdf/1705.05363.pdf + +This example: + - demonstrates how to write a custom RLModule, representing the ICM from the paper + above. Note that this custom RLModule does not belong to any individual agent. + - demonstrates how to write a custom (PPO) TorchLearner that a) adds the ICM to its + MultiRLModule, b) trains the regular PPO Policy plus the ICM module, using the + PPO parent loss and the ICM's RLModule's own loss function. + +We use a FrozenLake (sparse reward) environment with a custom map size of 12x12 and a +hard time step limit of 22 to make it almost impossible for a non-curiosity based +learners to learn a good policy. + + +How to run this script +---------------------- +`python [script file name].py --enable-new-api-stack` + +Use the `--no-curiosity` flag to disable curiosity learning and force your policy +to be trained on the task w/o the use of intrinsic rewards. With this option, the +algorithm should NOT succeed. + +For debugging, use the following additional command line options +`--no-tune --num-env-runners=0` +which should allow you to set breakpoints anywhere in the RLlib code and +have the execution stop there for inspection and debugging. +""" + +from DSSE import CoverageDroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllFlattenWrapper +from ray.tune.registry import register_env +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray import tune +from ray.rllib.algorithms.algorithm_config import AlgorithmConfig +from ray.rllib.algorithms.callbacks import DefaultCallbacks +from ray.rllib.connectors.env_to_module import FlattenObservations +from ray.rllib.algorithms.ppo import PPOConfig +from ray.rllib.policy.policy import PolicySpec +from icm_learners import ( + ICM_MODULE_ID, + PPOTorchLearnerWithCuriosity, + DQNTorchLearnerWithCuriosity, +) +from ray.rllib.core import DEFAULT_MODULE_ID +from ray.rllib.core.rl_module.multi_rl_module import MultiRLModuleSpec +from ray.rllib.core.rl_module.rl_module import RLModuleSpec +from ray.rllib.examples.rl_modules.classes.intrinsic_curiosity_model_rlm import ( + IntrinsicCuriosityModel, +) +from ray.rllib.utils.metrics import ( + ENV_RUNNER_RESULTS, + EPISODE_RETURN_MEAN, + NUM_ENV_STEPS_SAMPLED_LIFETIME, +) +from ray.rllib.utils.test_utils import ( + add_rllib_example_script_args, + run_rllib_example_script_experiment, +) + +parser = add_rllib_example_script_args( + default_iters=2000, + default_timesteps=10000000, + default_reward=0.9, +) +parser.set_defaults(enable_new_api_stack=True) + +# TODO: Study callbacks on the RLlib documentation +# TODO: Study the parser and runner functions +# TODO: Change the rest of the parameters +# TODO: Alter the stop condition + + +def env_creator(config, args): + print("-------------------------- ENV CREATOR --------------------------") + # 6 hours of simulation, 600 radius + env = CoverageDroneSwarmSearch( + timestep_limit=200, + drone_amount=2, + prob_matrix_path="../../../data/mat_9.npy", + ) + env = AllFlattenWrapper(env) + grid_size = env.grid_size + print("Grid size: ", grid_size) + positions = [ + (0, grid_size // 2), + (grid_size - 1, grid_size // 2), + ] + env = RetainDronePosWrapper(env, positions) + return env + + +ENV_NAME = "DsseCoverage" + +def main(_): + args = parser.parse_args() + assert args.num_agents > 0, "Must set --num-agents > 0 when running this script!" + assert ( + args.enable_new_api_stack + ), "Must set --enable-new-api-stack when running this script!" + + register_env(ENV_NAME, lambda config: ParallelPettingZooEnv(env_creator(config, args))) + + args.algo = "PPO" + base_config = ( + PPOConfig() + .environment(ENV_NAME) + .env_runners( + num_envs_per_env_runner=5, + # env_to_module_connector=lambda env: FlattenObservations(multi_agent=True), + ) + .training( + learner_config_dict={ + # Intrinsic reward coefficient. + "intrinsic_reward_coeff": 0.05, + # Forward loss weight (vs inverse dynamics loss). Total ICM loss is: + # L(total ICM) = ( + # `forward_loss_weight` * L(forward) + # + (1.0 - `forward_loss_weight`) * L(inverse_dyn) + # ) + "forward_loss_weight": 0.2, + } + ) + # TODO: Try to do this like in https://github.com/ray-project/ray/blob/master/rllib/examples/multi_agent/pettingzoo_independent_learning.py + .multi_agent( + policies={ + "default_policy": PolicySpec(), + }, + policy_mapping_fn=(lambda aid, *args, **kwargs: "default_policy"), + ) + .rl_module( + rl_module_spec=MultiRLModuleSpec( + rl_module_specs={ + # The "main" RLModule (policy) to be trained by our algo. + DEFAULT_MODULE_ID: RLModuleSpec( + model_config={ + "vf_share_layers": True, + "actor_critic_encoder_config": { + "fcnet_hiddens": [512, 256], + "fcnet_activation": "relu", + }, + } + ), + # The intrinsic curiosity model. + ICM_MODULE_ID: RLModuleSpec( + module_class=IntrinsicCuriosityModel, + # Only create the ICM on the Learner workers, NOT on the + # EnvRunners. + learner_only=True, + # Configure the architecture of the ICM here. + model_config={ + "feature_dim": 288, + "feature_net_hiddens": (256, 256), + "feature_net_activation": "relu", + "inverse_net_hiddens": (256, 256), + "inverse_net_activation": "relu", + "forward_net_hiddens": (256, 256), + "forward_net_activation": "relu", + }, + ), + } + ), + # Use a different learning rate for training the ICM. + algorithm_config_overrides_per_module={ + ICM_MODULE_ID: AlgorithmConfig.overrides(lr=0.0005) + }, + ) + .training( + num_epochs=6, + # Plug in the correct Learner class. + learner_class=PPOTorchLearnerWithCuriosity, + train_batch_size_per_learner=8000, + train_batch_size=8192 * 3, + lr=8e-6, + gamma=0.9999999, + lambda_=0.9, + use_gae=True, + entropy_coeff=0.01, + vf_clip_param=100000, + minibatch_size=300, + num_sgd_iter=10, + ) + ) + + stop = { + f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": 8000, # TODO: Check graph for this value + NUM_ENV_STEPS_SAMPLED_LIFETIME: 40_000_000, + } + + # TODO: Modify this script to be able to choose the storage path + run_rllib_example_script_experiment( + base_config, + args, + stop=stop, + ) + + +if __name__ == "__main__": + main(None) diff --git a/src/coverage/train/train_ppo_cnn_cov.py b/src/coverage/train/train_ppo_cnn_cov.py new file mode 100644 index 0000000..3da5544 --- /dev/null +++ b/src/coverage/train/train_ppo_cnn_cov.py @@ -0,0 +1,88 @@ +from DSSE import CoverageDroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper +import ray +from ray import tune +from ray.rllib.algorithms.ppo import PPOConfig +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from src.models.ppo_cnn import PpoCnnModel +from src.models.cnn_config import CNNConfig +from src.utils.random_position_wrapper import RandomPositionWrapper + + +def env_creator(params, args): + print("-------------------------- ENV CREATOR --------------------------") + env = CoverageDroneSwarmSearch( + timestep_limit=200, + drone_amount=args.n_agents, + prob_matrix_path=args.matrix_path, + ) + + grid_size = env.grid_size + print("Grid size: ", grid_size) + + if args.use_random_positions: + env = RandomPositionWrapper(env) + else: + env = RetainDronePosWrapper(env, position_on_edges(grid_size, args.n_agents)) + + return env + + +def position_on_edges(grid_size, n_agents): + positions = [ + (0, grid_size // 2), + (grid_size - 1, grid_size // 2), + (grid_size // 2, 0), + (grid_size // 2, grid_size - 1), + ] + return positions[0:n_agents] + + +def main(args): + ray.init() + + env_name = "DSSE_Coverage" + + register_env( + env_name, lambda config: ParallelPettingZooEnv(env_creator(config, args)) + ) + model = PpoCnnModel + model.CONFIG = CNNConfig(kernel_sizes=[(3, 3), (2, 2)]) + ModelCatalog.register_custom_model(model.NAME, model) + + config = ( + PPOConfig() + .environment(env=env_name) + .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") + .training( + train_batch_size=8192 * 5, + lr=6e-6, + gamma=0.9999999, + lambda_=0.9, + use_gae=True, + entropy_coeff=0.01, + vf_clip_param=100000, + minibatch_size=300, + num_sgd_iter=10, + model={ + "custom_model": "CNNModel", + "_disable_preprocessor_api": True, + }, + ) + .experimental(_disable_preprocessor_api=True) + .debugging(log_level="ERROR") + .framework(framework="torch") + .resources(num_gpus=1) + ) + + tune.run( + "PPO", + name="PPO_" + args.exp_name, + # resume=True, + stop={"timesteps_total": 20_000_000}, + checkpoint_freq=20, + storage_path=args.storage_path + env_name, + config=config.to_dict(), + ) diff --git a/src/train_ppo_mlp_cov.py b/src/coverage/train/train_ppo_mlp_cov.py similarity index 53% rename from src/train_ppo_mlp_cov.py rename to src/coverage/train/train_ppo_mlp_cov.py index 8bc6567..7391d1a 100644 --- a/src/train_ppo_mlp_cov.py +++ b/src/coverage/train/train_ppo_mlp_cov.py @@ -1,4 +1,3 @@ -import pathlib from DSSE import CoverageDroneSwarmSearch from DSSE.environment.wrappers import RetainDronePosWrapper, AllFlattenWrapper import ray @@ -6,60 +5,54 @@ from ray.rllib.algorithms.ppo import PPOConfig from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv from ray.tune.registry import register_env -from torch import nn -import torch -import numpy as np +from src.utils.random_position_wrapper import RandomPositionWrapper -def env_creator(args): +def env_creator(params, args): print("-------------------------- ENV CREATOR --------------------------") - N_AGENTS = 2 - # 6 hours of simulation, 600 radius env = CoverageDroneSwarmSearch( - timestep_limit=200, drone_amount=N_AGENTS, prob_matrix_path="min_matrix.npy" + timestep_limit=200, + drone_amount=args.n_agents, + prob_matrix_path=args.matrix_path, ) env = AllFlattenWrapper(env) + grid_size = env.grid_size print("Grid size: ", grid_size) - positions = [ - (0, grid_size // 2), - (grid_size - 1, grid_size // 2), - ] - env = RetainDronePosWrapper(env, positions) - return env -def position_on_diagonal(grid_size, drone_amount): - positions = [] - center = grid_size // 2 - for i in range(-drone_amount // 2, drone_amount // 2): - positions.append((center + i, center + i)) - return positions + if args.use_random_positions: + env = RandomPositionWrapper(env) + else: + env = RetainDronePosWrapper(env, position_on_edges(grid_size, args.n_agents)) -def position_on_circle(grid_size, drone_amount, radius): - positions = [] - center = grid_size // 2 - angle_increment = 2 * np.pi / drone_amount + return env - for i in range(drone_amount): - angle = i * angle_increment - x = center + int(radius * np.cos(angle)) - y = center + int(radius * np.sin(angle)) - positions.append((x, y)) - return positions +def position_on_edges(grid_size, n_agents): + positions = [ + (0, grid_size // 2), + (grid_size - 1, grid_size // 2), + (grid_size // 2, 0), + (grid_size // 2, grid_size - 1), + ] + return positions[0:n_agents] -if __name__ == "__main__": +def main(args): ray.init() env_name = "DSSE_Coverage" - register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) + register_env( + env_name, lambda config: ParallelPettingZooEnv(env_creator(config, args)) + ) config = ( PPOConfig() .environment(env=env_name) - .rollouts(num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=4) + .rollouts( + num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=4 + ) .training( train_batch_size=8192 * 3, lr=8e-6, @@ -68,7 +61,7 @@ def position_on_circle(grid_size, drone_amount, radius): use_gae=True, entropy_coeff=0.01, vf_clip_param=100000, - sgd_minibatch_size=300, + minibatch_size=300, num_sgd_iter=10, model={ "fcnet_hiddens": [512, 256], @@ -80,13 +73,12 @@ def position_on_circle(grid_size, drone_amount, radius): .resources(num_gpus=1) ) - curr_path = pathlib.Path().resolve() tune.run( "PPO", - name="PPO_" + input("Exp name: "), + name="PPO_" + args.exp_name, # resume=True, stop={"timesteps_total": 40_000_000}, checkpoint_freq=25, - storage_path=f"{curr_path}/ray_res/" + env_name, + storage_path=args.storage_path + env_name, config=config.to_dict(), ) diff --git a/src/greedy_heuristic.py b/src/greedy_heuristic.py index 19904d5..f6a9042 100644 --- a/src/greedy_heuristic.py +++ b/src/greedy_heuristic.py @@ -2,7 +2,7 @@ from DSSE import Actions from DSSE import DroneSwarmSearch from DSSE.environment.wrappers import RetainDronePosWrapper -from recorder import PygameRecord +from .utils.recorder import PygameRecord class GreedyAgent: @@ -89,10 +89,12 @@ def drones_colide(self, drones_positions: dict, new_drone_position: tuple) -> bo def __repr__(self) -> str: return "greedy" - + see = input("Want to see ?? (y/n): ") see = see.lower() == "y" + + def env_creator(_): env = DroneSwarmSearch( render_mode="human" if see else "ansi", @@ -111,6 +113,7 @@ def env_creator(_): env = RetainDronePosWrapper(env, positions) return env + greedy_agent = GreedyAgent() env = env_creator(None) diff --git a/src/models/__init__.py b/src/models/__init__.py new file mode 100644 index 0000000..2c45483 --- /dev/null +++ b/src/models/__init__.py @@ -0,0 +1,7 @@ +from .ppo_cnn import PpoCnnModel +from .ppo_cnn_lstm import PpoCnnLstmModel + +__all__ = [ + "PpoCnnModel", + "PpoCnnLstmModel", +] diff --git a/src/models/cnn_config.py b/src/models/cnn_config.py new file mode 100644 index 0000000..ce0b056 --- /dev/null +++ b/src/models/cnn_config.py @@ -0,0 +1,15 @@ +from dataclasses import dataclass, field + + +@dataclass +class CNNConfig: + kernel_sizes: list = field(default_factory=lambda : [(3, 3), (2, 2)]) + out_channels: list = field(default_factory=lambda : [16, 32]) + + def get_flatten_size(self, obs_space_shape): + dim_0 = obs_space_shape[0] + dim_1 = obs_space_shape[1] + for kernel_size in self.kernel_sizes: + dim_0 = (dim_0 - kernel_size[0]) + 1 + dim_1 = (dim_1 - kernel_size[1]) + 1 + return self.out_channels[-1] * dim_0 * dim_1 diff --git a/src/models/ppo_cnn.py b/src/models/ppo_cnn.py new file mode 100644 index 0000000..a99fabb --- /dev/null +++ b/src/models/ppo_cnn.py @@ -0,0 +1,77 @@ +import torch.nn as nn +import torch +from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 +from .cnn_config import CNNConfig + +class PpoCnnModel(TorchModelV2, nn.Module): + NAME = "PpoCnnModel" + CONFIG = CNNConfig() + + def __init__( + self, + obs_space, + act_space, + num_outputs, + model_config, + name, + **kw, + ): + print("OBSSPACE: ", obs_space) + TorchModelV2.__init__( + self, obs_space, act_space, num_outputs, model_config, name, **kw + ) + nn.Module.__init__(self) + + flatten_size = self.CONFIG.get_flatten_size(obs_space[1].shape) + kernels = self.CONFIG.kernel_sizes + self.cnn = nn.Sequential( + nn.Conv2d( + in_channels=1, + out_channels=self.CONFIG.out_channels[0], + kernel_size=kernels[0], + stride=(1, 1), + ), + nn.Tanh(), + nn.Conv2d( + in_channels=self.CONFIG.out_channels[0], + out_channels=self.CONFIG.out_channels[1], + kernel_size=kernels[1], + stride=(1, 1), + ), + nn.Tanh(), + nn.Flatten(), + nn.Linear(flatten_size, 256), + nn.Tanh(), + ) + + self.linear = nn.Sequential( + nn.Linear(obs_space[0].shape[0], 512), + nn.Tanh(), + nn.Linear(512, 256), + nn.Tanh(), + ) + + self.join = nn.Sequential( + nn.Linear(256 * 2, 256), + nn.Tanh(), + ) + + self.policy_fn = nn.Linear(256, num_outputs) + self.value_fn = nn.Linear(256, 1) + + def forward(self, input_dict, state, seq_lens): + input_positions = input_dict["obs"][0].float() + input_matrix = input_dict["obs"][1].float() + + input_matrix = input_matrix.unsqueeze(1) + cnn_out = self.cnn(input_matrix) + linear_out = self.linear(input_positions) + + value_input = torch.cat((cnn_out, linear_out), dim=1) + value_input = self.join(value_input) + + self._value_out = self.value_fn(value_input) + return self.policy_fn(value_input), state + + def value_function(self): + return self._value_out.flatten() diff --git a/src/train_ppo_cnn_lstm.py b/src/models/ppo_cnn_lstm.py similarity index 61% rename from src/train_ppo_cnn_lstm.py rename to src/models/ppo_cnn_lstm.py index 89d7fdb..388b136 100644 --- a/src/train_ppo_cnn_lstm.py +++ b/src/models/ppo_cnn_lstm.py @@ -1,23 +1,14 @@ -import os -import pathlib -from DSSE import DroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -from DSSE.environment.wrappers.communication_wrapper import CommunicationWrapper -import ray -from ray import tune -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog from ray.rllib.models.torch.recurrent_net import RecurrentNetwork as TorchRNN from ray.rllib.models.modelv2 import ModelV2 from ray.rllib.utils.annotations import override from ray.rllib.policy.rnn_sequencing import add_time_dimension -from ray.tune.registry import register_env from torch import nn import torch -class CNNModel(TorchRNN, nn.Module): +class PpoCnnLstmModel(TorchRNN, nn.Module): + NAME = "PPO_CNN_LSTM" + def __init__( self, obs_space, @@ -32,7 +23,9 @@ def __init__( nn.Module.__init__(self) super().__init__(obs_space, act_space, num_outputs, model_config, name, **kw) - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[0] - 7 - 3) + flatten_size = ( + 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[0] - 7 - 3) + ) self.cnn = nn.Sequential( nn.Conv2d( in_channels=1, @@ -75,13 +68,13 @@ def get_initial_state(self): # Place hidden states on same device as model. h = [ self.linear.weight.new(1, self.lstm_state_size).zero_().squeeze(0), - self.linear.weight.new(1, self.lstm_state_size).zero_().squeeze(0) + self.linear.weight.new(1, self.lstm_state_size).zero_().squeeze(0), ] return h - + def value_function(self): return self._value_out.flatten() - + @override(ModelV2) def forward( self, @@ -114,10 +107,10 @@ def forward( value_input = torch.cat((cnn_out, lstm_out), dim=1) value_input = self.join(value_input) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), new_state - + @override(TorchRNN) def forward_rnn(self, inputs, state, seq_lens): """Feeds `inputs` (B x T x ..) through the Gru Unit. @@ -132,77 +125,8 @@ def forward_rnn(self, inputs, state, seq_lens): """ linear_out = nn.functional.tanh(self.linear(inputs)) - lstm_out, [h, c] = self.lstm(linear_out, [torch.unsqueeze(state[0], 0), torch.unsqueeze(state[1], 0)]) + lstm_out, [h, c] = self.lstm( + linear_out, [torch.unsqueeze(state[0], 0), torch.unsqueeze(state[1], 0)] + ) return lstm_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)] - - -def env_creator(args): - """ - Petting Zoo environment for search of shipwrecked people. - check it out at - https://github.com/pfeinsper/drone-swarm-search - or install with - pip install DSSE - """ - env = DroneSwarmSearch( - drone_amount=4, - grid_size=40, - dispersion_inc=0.1, - person_initial_position=(20, 20), - ) - positions = [ - (20, 0), - (20, 39), - (0, 20), - (39, 20), - ] - env = AllPositionsWrapper(env) - env = RetainDronePosWrapper(env, positions) - return env - - -if __name__ == "__main__": - ray.init() - - env_name = "DSSE" - - register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) - ModelCatalog.register_custom_model("CNNModel", CNNModel) - - config = ( - PPOConfig() - .environment(env=env_name) - .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") - .training( - train_batch_size=4096, - lr=1e-5, - gamma=0.9999999, - lambda_=0.9, - use_gae=True, - entropy_coeff=0.01, - sgd_minibatch_size=300, - num_sgd_iter=10, - model={ - "custom_model": "CNNModel", - "use_lstm": False, - "lstm_cell_size": 256, - "_disable_preprocessor_api": True, - }, - ) - .experimental(_disable_preprocessor_api=True) - .debugging(log_level="ERROR") - .framework(framework="torch") - .resources(num_gpus=1) - ) - - curr_path = pathlib.Path().resolve() - tune.run( - "PPO", - name="PPO_LSTM_M", - resume=True, - stop={"timesteps_total": 20_000_000, "episode_reward_mean": 1.82}, - checkpoint_freq=15, - storage_path=f"{curr_path}/ray_res/" + env_name, - config=config.to_dict(), - ) diff --git a/src/search/test/test_ppo_cnn.py b/src/search/test/test_ppo_cnn.py new file mode 100644 index 0000000..60189f1 --- /dev/null +++ b/src/search/test/test_ppo_cnn.py @@ -0,0 +1,49 @@ +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +import ray +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from ray.rllib.algorithms.ppo import PPO +from models import PpoCnnModel +from src.utils.play_env import play_with_record, evaluate_agent_search + + +# DEFINE HERE THE EXACT ENVIRONMENT YOU USED TO TRAIN THE AGENT +def env_creator(_): + render_mode = "human" if args.see else "ansi" + env = DroneSwarmSearch( + drone_amount=4, + grid_size=40, + render_mode=render_mode, + render_grid=True, + dispersion_inc=0.1, + person_initial_position=(20, 20), + ) + positions = [ + (20, 0), + (20, 39), + (0, 20), + (39, 20), + ] + env = RetainDronePosWrapper(env, positions) + env = AllPositionsWrapper(env) + return env + +def main(args): + model = PpoCnnModel + ModelCatalog.register_custom_model(model.NAME, model) + + env = env_creator(None) + register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) + ray.init() + + checkpoint_path = args.checkpoint + PPOagent = PPO.from_checkpoint(checkpoint_path) + + if args.see: + play_with_record(env, PPOagent) + else: + evaluate_agent_search(env, PPOagent) + + env.close() diff --git a/src/search/test/test_trained_cnn_lstm.py b/src/search/test/test_trained_cnn_lstm.py new file mode 100644 index 0000000..6aa0c15 --- /dev/null +++ b/src/search/test/test_trained_cnn_lstm.py @@ -0,0 +1,56 @@ +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +import ray +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from ray.rllib.algorithms.ppo import PPO +from models import PpoCnnLstmModel +from src.utils.play_env import play_with_record, evaluate_agent_search + + +def main(args): + def env_creator(_): + """ + Petting Zoo environment for search of shipwrecked people. + check it out at + https://github.com/pfeinsper/drone-swarm-search + or install with + pip install DSSE + """ + render_mode = "human" if args.see else "ansi" + env = DroneSwarmSearch( + drone_amount=4, + grid_size=40, + dispersion_inc=0.1, + person_initial_position=(20, 20), + render_mode=render_mode, + render_grid=True, + ) + positions = [ + (20, 0), + (20, 39), + (0, 20), + (39, 20), + ] + env = AllPositionsWrapper(env) + env = RetainDronePosWrapper(env, positions) + return env + + + env = env_creator(None) + register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) + model = PpoCnnLstmModel + ModelCatalog.register_custom_model(model.NAME, model) + ray.init() + + + checkpoint_path = args.checkpoint + PPOagent = PPO.from_checkpoint(checkpoint_path) + + if args.see: + play_with_record(env, PPOagent) + else: + evaluate_agent_search(env, PPOagent) + + env.close() diff --git a/src/search/test/test_trained_communication.py b/src/search/test/test_trained_communication.py new file mode 100644 index 0000000..6a851b2 --- /dev/null +++ b/src/search/test/test_trained_communication.py @@ -0,0 +1,48 @@ +from src.utils.play_env import play_with_record, evaluate_agent_search +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +from DSSE.environment.wrappers.communication_wrapper import CommunicationWrapper +import ray +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from ray.rllib.algorithms.ppo import PPO +from models import PpoCnnModel + + +def main(args): + model = PpoCnnModel + ModelCatalog.register_custom_model(model.NAME, model) + + # DEFINE HERE THE EXACT ENVIRONMENT YOU USED TO TRAIN THE AGENT + def env_creator(_): + env = DroneSwarmSearch( + drone_amount=4, + grid_size=40, + dispersion_inc=0.1, + person_initial_position=(20, 20), + ) + positions = [ + (20, 0), + (20, 39), + (0, 20), + (39, 20), + ] + env = AllPositionsWrapper(env) + env = CommunicationWrapper(env, n_steps=12) + env = RetainDronePosWrapper(env, positions) + return env + + env = env_creator(None) + register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) + ray.init() + + checkpoint_path = args.checkpoint + PPOagent = PPO.from_checkpoint(checkpoint_path) + + if args.see: + play_with_record(env, PPOagent) + else: + evaluate_agent_search(env, PPOagent) + + env.close() diff --git a/src/train_descentralized_ppo_cnn.py b/src/search/train/train_descentralized_ppo_cnn.py similarity index 97% rename from src/train_descentralized_ppo_cnn.py rename to src/search/train/train_descentralized_ppo_cnn.py index dbe144a..1d9ce30 100644 --- a/src/train_descentralized_ppo_cnn.py +++ b/src/search/train/train_descentralized_ppo_cnn.py @@ -1,4 +1,3 @@ -import os import pathlib from DSSE import DroneSwarmSearch from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper @@ -31,7 +30,9 @@ def __init__( ) nn.Module.__init__(self) - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + flatten_size = ( + 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + ) self.cnn = nn.Sequential( nn.Conv2d( in_channels=1, @@ -63,7 +64,7 @@ def __init__( nn.Linear(256 * 2, 256), nn.Tanh(), ) - + self.policy_fn = nn.Linear(256, num_outputs) self.value_fn = nn.Linear(256, 1) @@ -77,7 +78,7 @@ def forward(self, input_dict, state, seq_lens): value_input = torch.cat((cnn_out, linear_out), dim=1) value_input = self.join(value_input) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), state @@ -85,7 +86,6 @@ def value_function(self): return self._value_out.flatten() - def env_creator(args): env = DroneSwarmSearch( drone_amount=4, diff --git a/src/train_dqn_cnn.py b/src/search/train/train_dqn_cnn.py similarity index 92% rename from src/train_dqn_cnn.py rename to src/search/train/train_dqn_cnn.py index 028f454..d9b216a 100644 --- a/src/train_dqn_cnn.py +++ b/src/search/train/train_dqn_cnn.py @@ -1,7 +1,7 @@ -import os import pathlib -from drone_swarm_search.DSSE import DroneSwarmSearch -from drone_swarm_search.DSSE.environment.wrappers import AllPositionsWrapper, RetainDronePosWrapper, TopNProbsWrapper +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import AllPositionsWrapper, RetainDronePosWrapper + # from DSSE.environment.wrappers import AllPositionsWrapper import ray from ray import tune @@ -12,7 +12,7 @@ from ray.tune.registry import register_env import torch from torch import nn -import random + class CNNModel(TorchModelV2, nn.Module): def __init__( @@ -30,7 +30,9 @@ def __init__( ) nn.Module.__init__(self) - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + flatten_size = ( + 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + ) self.cnn = nn.Sequential( nn.Conv2d( in_channels=1, @@ -62,7 +64,7 @@ def __init__( nn.Linear(256 * 2, 256), nn.Tanh(), ) - + self.policy_fn = nn.Linear(256, num_outputs) self.value_fn = nn.Linear(256, 1) @@ -76,13 +78,14 @@ def forward(self, input_dict, state, seq_lens): value_input = torch.cat((cnn_out, linear_out), dim=1) value_input = self.join(value_input) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), state def value_function(self): return self._value_out.flatten() + def env_creator(args): env = DroneSwarmSearch( drone_amount=4, @@ -109,7 +112,7 @@ def env_creator(args): register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) ModelCatalog.register_custom_model("CNNModel", CNNModel) - natural_value = 512/(14*20*1) + natural_value = 512 / (14 * 20 * 1) config = ( DQNConfig() .exploration( @@ -152,6 +155,6 @@ def env_creator(args): storage_path=f"{curr_path}/ray_res/" + env_name, config=config.to_dict(), ) - + # Finalize Ray to free up resources ray.shutdown() diff --git a/src/train_dqn_multi.py b/src/search/train/train_dqn_multi.py similarity index 89% rename from src/train_dqn_multi.py rename to src/search/train/train_dqn_multi.py index 0273f6c..957e645 100644 --- a/src/train_dqn_multi.py +++ b/src/search/train/train_dqn_multi.py @@ -47,17 +47,11 @@ def __init__( nn.ReLU(), # nn.MaxPool2d(kernel_size=2), nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1) + in_channels=16, out_channels=32, kernel_size=(4, 4), stride=(1, 1) ), nn.ReLU(), nn.Conv2d( - in_channels=32, - out_channels=64, - kernel_size=(3, 3), - stride=(1, 1) + in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1) ), # nn.MaxPool2d(kernel_size=2), nn.Flatten(), @@ -73,7 +67,7 @@ def __init__( nn.ReLU(), nn.LayerNorm(512), ) - + self.unifier = nn.Sequential( nn.Linear(512 * 2, 512), nn.ReLU(), @@ -86,13 +80,13 @@ def forward(self, input_dict, state, seq_lens): # Convert dims input_cnn = input_[1].unsqueeze(1) model_out = self.conv1(input_cnn) - + scalar_input = input_[0].float() scalar_out = self.fc_scalar(scalar_input) value_input = torch.cat((model_out, scalar_out), -1) value_input = self.unifier(value_input) - + return self.policy_fn(value_input), state @@ -118,7 +112,9 @@ def env_creator(args): config = DQNConfig() config = config.environment(env=env_name) - config = config.rollouts(num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=2) + config = config.rollouts( + num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=2 + ) config = config.training( train_batch_size=512, grad_clip=None, @@ -151,8 +147,4 @@ def env_creator(args): storage_path=f"{curr_path}/ray_res/" + env_name, checkpoint_config=air.CheckpointConfig(checkpoint_frequency=10), ) - tune.Tuner( - "DQN", - run_config=run_config, - param_space=config.to_dict() - ).fit() + tune.Tuner("DQN", run_config=run_config, param_space=config.to_dict()).fit() diff --git a/src/search/train/train_ppo_cnn.py b/src/search/train/train_ppo_cnn.py new file mode 100644 index 0000000..beba438 --- /dev/null +++ b/src/search/train/train_ppo_cnn.py @@ -0,0 +1,80 @@ +import os +import pathlib +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +import ray +from ray import tune +from ray.rllib.algorithms.ppo import PPOConfig +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from models import PpoCnnModel + + +def env_creator(args): + env = DroneSwarmSearch( + drone_amount=4, + grid_size=40, + dispersion_inc=0.1, + person_initial_position=(20, 20), + ) + positions = [ + (20, 0), + (20, 39), + (0, 20), + (39, 20), + ] + env = AllPositionsWrapper(env) + env = RetainDronePosWrapper(env, positions) + return env + + +if __name__ == "__main__": + ray.init() + + env_name = "DSSE" + + model = PpoCnnModel + register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) + ModelCatalog.register_custom_model(model.NAME, model) + + config = ( + PPOConfig() + .environment(env=env_name) + .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") + .training( + train_batch_size=8192, + lr=1e-5, + gamma=0.9999999, + lambda_=0.9, + use_gae=True, + # clip_param=0.3, + # grad_clip=None, + entropy_coeff=0.01, + # vf_loss_coeff=0.25, + # vf_clip_param=10, + sgd_minibatch_size=300, + num_sgd_iter=10, + model={ + "custom_model": "CNNModel", + "_disable_preprocessor_api": True, + }, + ) + .experimental(_disable_preprocessor_api=True) + .debugging(log_level="ERROR") + .framework(framework="torch") + .resources(num_gpus=1) + ) + + curr_path = pathlib.Path().resolve() + tune.run( + "PPO", + name="PPO", + stop={ + "timesteps_total": 20_000_000 if not os.environ.get("CI") else 50000, + "episode_reward_mean": 1.75, + }, + checkpoint_freq=10, + storage_path=f"{curr_path}/ray_res/" + env_name, + config=config.to_dict(), + ) diff --git a/src/train_ppo_cnn_comm.py b/src/search/train/train_ppo_cnn_comm.py similarity index 91% rename from src/train_ppo_cnn_comm.py rename to src/search/train/train_ppo_cnn_comm.py index 8bd2b98..5a72c91 100644 --- a/src/train_ppo_cnn_comm.py +++ b/src/search/train/train_ppo_cnn_comm.py @@ -30,7 +30,9 @@ def __init__( ) nn.Module.__init__(self) - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + flatten_size = ( + 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) + ) self.cnn = nn.Sequential( nn.Conv2d( in_channels=1, @@ -62,7 +64,7 @@ def __init__( nn.Linear(256 * 2, 256), nn.Tanh(), ) - + self.policy_fn = nn.Linear(256, num_outputs) self.value_fn = nn.Linear(256, 1) @@ -76,7 +78,7 @@ def forward(self, input_dict, state, seq_lens): value_input = torch.cat((cnn_out, linear_out), dim=1) value_input = self.join(value_input) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), state @@ -114,7 +116,9 @@ def env_creator(args): config = ( PPOConfig() .environment(env=env_name) - .rollouts(num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=2) + .rollouts( + num_rollout_workers=6, rollout_fragment_length="auto", num_envs_per_worker=2 + ) .training( train_batch_size=8192, lr=1e-5, @@ -143,7 +147,10 @@ def env_creator(args): tune.run( "PPO", name="PPO_COMM_WRAPPER", - stop={"timesteps_total": 20_000_000 if not os.environ.get("CI") else 50000, "episode_reward_mean": 1.75}, + stop={ + "timesteps_total": 20_000_000 if not os.environ.get("CI") else 50000, + "episode_reward_mean": 1.75, + }, checkpoint_freq=10, storage_path=f"{curr_path}/ray_res/" + env_name, config=config.to_dict(), diff --git a/src/search/train/train_ppo_cnn_lstm.py b/src/search/train/train_ppo_cnn_lstm.py new file mode 100644 index 0000000..c6c760d --- /dev/null +++ b/src/search/train/train_ppo_cnn_lstm.py @@ -0,0 +1,82 @@ +import pathlib +from DSSE import DroneSwarmSearch +from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper +import ray +from ray import tune +from ray.rllib.algorithms.ppo import PPOConfig +from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv +from ray.rllib.models import ModelCatalog +from ray.tune.registry import register_env +from models import PpoCnnLstmModel + + +def env_creator(args): + """ + Petting Zoo environment for search of shipwrecked people. + check it out at + https://github.com/pfeinsper/drone-swarm-search + or install with + pip install DSSE + """ + env = DroneSwarmSearch( + drone_amount=4, + grid_size=40, + dispersion_inc=0.1, + person_initial_position=(20, 20), + ) + positions = [ + (20, 0), + (20, 39), + (0, 20), + (39, 20), + ] + env = AllPositionsWrapper(env) + env = RetainDronePosWrapper(env, positions) + return env + + +if __name__ == "__main__": + ray.init() + + env_name = "DSSE" + + model = PpoCnnLstmModel + register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) + ModelCatalog.register_custom_model(model.NAME, model) + + config = ( + PPOConfig() + .environment(env=env_name) + .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") + .training( + train_batch_size=4096, + lr=1e-5, + gamma=0.9999999, + lambda_=0.9, + use_gae=True, + entropy_coeff=0.01, + sgd_minibatch_size=300, + num_sgd_iter=10, + model={ + "custom_model": "CNNModel", + "use_lstm": False, + "lstm_cell_size": 256, + "_disable_preprocessor_api": True, + }, + ) + .experimental(_disable_preprocessor_api=True) + .debugging(log_level="ERROR") + .framework(framework="torch") + .resources(num_gpus=1) + ) + + curr_path = pathlib.Path().resolve() + tune.run( + "PPO", + name="PPO_LSTM_M", + resume=True, + stop={"timesteps_total": 20_000_000, "episode_reward_mean": 1.82}, + checkpoint_freq=15, + storage_path=f"{curr_path}/ray_res/" + env_name, + config=config.to_dict(), + ) diff --git a/src/train_ppo_encoded.py b/src/search/train/train_ppo_encoded.py similarity index 93% rename from src/train_ppo_encoded.py rename to src/search/train/train_ppo_encoded.py index 355fc1e..0483f71 100644 --- a/src/train_ppo_encoded.py +++ b/src/search/train/train_ppo_encoded.py @@ -8,7 +8,6 @@ from ray.rllib.models import ModelCatalog from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 from ray.tune.registry import register_env -import torch from torch import nn @@ -42,17 +41,11 @@ def __init__( nn.ReLU(), # nn.MaxPool2d(kernel_size=2), nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1) + in_channels=16, out_channels=32, kernel_size=(4, 4), stride=(1, 1) ), nn.ReLU(), nn.Conv2d( - in_channels=32, - out_channels=64, - kernel_size=(3, 3), - stride=(1, 1) + in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1) ), # nn.MaxPool2d(kernel_size=2), nn.Flatten(), @@ -104,7 +97,7 @@ def env_creator(args): config = ( PPOConfig() .environment(env=env_name) - .rollouts(num_rollout_workers=5, rollout_fragment_length='auto') + .rollouts(num_rollout_workers=5, rollout_fragment_length="auto") .training( train_batch_size=512, lr=2e-5, diff --git a/src/train_ppo_mlp.py b/src/search/train/train_ppo_mlp.py similarity index 99% rename from src/train_ppo_mlp.py rename to src/search/train/train_ppo_mlp.py index bdd2781..e8a98ad 100644 --- a/src/train_ppo_mlp.py +++ b/src/search/train/train_ppo_mlp.py @@ -9,7 +9,6 @@ from ray.rllib.models import ModelCatalog from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 from ray.tune.registry import register_env -import torch from torch import nn @@ -41,7 +40,7 @@ def __init__( def forward(self, input_dict, state, seq_lens): input_ = input_dict["obs"].float() value_input = self.model(input_) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), state diff --git a/src/train_ppo_multi.py b/src/search/train/train_ppo_multi.py similarity index 93% rename from src/train_ppo_multi.py rename to src/search/train/train_ppo_multi.py index 51a2b23..71aea11 100644 --- a/src/train_ppo_multi.py +++ b/src/search/train/train_ppo_multi.py @@ -46,17 +46,11 @@ def __init__( nn.ReLU(), # nn.MaxPool2d(kernel_size=2), nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1) + in_channels=16, out_channels=32, kernel_size=(4, 4), stride=(1, 1) ), nn.ReLU(), nn.Conv2d( - in_channels=32, - out_channels=64, - kernel_size=(3, 3), - stride=(1, 1) + in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1) ), # nn.MaxPool2d(kernel_size=2), nn.Flatten(), @@ -72,7 +66,7 @@ def __init__( nn.ReLU(), nn.LayerNorm(512), ) - + self.unifier = nn.Sequential( nn.Linear(512 * 2, 512), nn.ReLU(), @@ -86,13 +80,13 @@ def forward(self, input_dict, state, seq_lens): # Convert dims input_cnn = input_[1].unsqueeze(1) model_out = self.conv1(input_cnn) - + scalar_input = input_[0].float() scalar_out = self.fc_scalar(scalar_input) value_input = torch.cat((model_out, scalar_out), -1) value_input = self.unifier(value_input) - + self._value_out = self.value_fn(value_input) return self.policy_fn(value_input), state diff --git a/src/test_ppo_cnn.py b/src/test_ppo_cnn.py deleted file mode 100644 index 1ad4001..0000000 --- a/src/test_ppo_cnn.py +++ /dev/null @@ -1,171 +0,0 @@ -import os -import pathlib -from recorder import PygameRecord -from DSSE import DroneSwarmSearch -from DSSE.environment.wrappers import TopNProbsWrapper, RetainDronePosWrapper, AllFlattenWrapper, AllPositionsWrapper -import ray -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env -from ray.rllib.algorithms.ppo import PPO -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -import torch.nn as nn -import argparse -import torch - - - -argparser = argparse.ArgumentParser() -argparser.add_argument("--checkpoint", type=str, required=True) -argparser.add_argument("--see", action="store_true", default=False) -args = argparser.parse_args() - - -class CNNModel(TorchModelV2, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - TorchModelV2.__init__( - self, obs_space, act_space, num_outputs, model_config, name, **kw - ) - nn.Module.__init__(self) - - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(8, 8), - stride=(1, 1), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Sequential( - nn.Linear(obs_space[0].shape[0], 512), - nn.Tanh(), - nn.Linear(512, 256), - nn.Tanh(), - ) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - - def forward(self, input_dict, state, seq_lens): - input_positions = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - linear_out = self.linear(input_positions) - - value_input = torch.cat((cnn_out, linear_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), state - - def value_function(self): - return self._value_out.flatten() - - - -# ModelCatalog.register_custom_model("MLPModel", MLPModel) -ModelCatalog.register_custom_model("CNNModel", CNNModel) - -# DEFINE HERE THE EXACT ENVIRONMENT YOU USED TO TRAIN THE AGENT -def env_creator(_): - render_mode = "human" if args.see else "ansi" - env = DroneSwarmSearch( - drone_amount=4, - grid_size=40, - render_mode=render_mode, - render_grid=True, - dispersion_inc=0.1, - person_initial_position=(20, 20), - ) - positions = [ - (20, 0), - (20, 39), - (0, 20), - (39, 20), - ] - env = RetainDronePosWrapper(env, positions) - env = AllPositionsWrapper(env) - return env - -env = env_creator(None) -register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) -ray.init() - - -checkpoint_path = args.checkpoint -PPOagent = PPO.from_checkpoint(checkpoint_path) - -reward_sum = 0 -i = 0 - -if args.see: - obs, info = env.reset() - with PygameRecord("test_trained.gif", 5) as rec: - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rec.add_frame() -else: - rewards = [] - founds = 0 - N_EVALS = 1000 - for _ in range(N_EVALS): - obs, info = env.reset() - reward_sum = 0 - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs, explore=False) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rewards.append(reward_sum) - for _, v in info.items(): - if v["Found"]: - founds += 1 - break - print("Average reward: ", sum(rewards) / N_EVALS) - print("Found %: ", founds / N_EVALS) - -print("Total reward: ", reward_sum) -print("Total steps: ", i) -print("Found: ", info) -env.close() diff --git a/src/test_trained_cnn_lstm.py b/src/test_trained_cnn_lstm.py deleted file mode 100644 index d488edd..0000000 --- a/src/test_trained_cnn_lstm.py +++ /dev/null @@ -1,243 +0,0 @@ -import os -import pathlib -from recorder import PygameRecord -from DSSE import DroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -import ray -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env -from ray.rllib.utils.annotations import override -from ray.rllib.policy.rnn_sequencing import add_time_dimension -from ray.rllib.algorithms.ppo import PPO -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -from ray.rllib.models.torch.recurrent_net import RecurrentNetwork as TorchRNN -from ray.rllib.models.modelv2 import ModelV2 -import torch.nn as nn -import argparse -import torch -import numpy as np - - - -argparser = argparse.ArgumentParser() -argparser.add_argument("--checkpoint", type=str, required=True) -argparser.add_argument("--see", action="store_true", default=False) -args = argparser.parse_args() - -class CNNModel(TorchRNN, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - num_outputs = act_space.n - nn.Module.__init__(self) - super().__init__(obs_space, act_space, num_outputs, model_config, name, **kw) - - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[0] - 7 - 3) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(8, 8), - stride=(1, 1), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Linear(obs_space[0].shape[0], 512) - - self.lstm_state_size = 256 - self.lstm = nn.LSTM(512, self.lstm_state_size, batch_first=True) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - print("NUM OUTPUTS: ", num_outputs) - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - # Holds the current "base" output (before logits layer). - self._value_out = None - - @override(ModelV2) - def get_initial_state(self): - # TODO: (sven): Get rid of `get_initial_state` once Trajectory - # View API is supported across all of RLlib. - # Place hidden states on same device as model. - h = [ - self.linear.weight.new(1, self.lstm_state_size).zero_().squeeze(0), - self.linear.weight.new(1, self.lstm_state_size).zero_().squeeze(0) - ] - return h - - def value_function(self): - return self._value_out.flatten() - - @override(ModelV2) - def forward( - self, - input_dict, - state, - seq_lens, - ): - """Adds time dimension to batch before sending inputs to forward_rnn(). - - You should implement forward_rnn() in your subclass.""" - scalar_inputs = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - - flat_inputs = scalar_inputs.flatten(start_dim=1) - # Note that max_seq_len != input_dict.max_seq_len != seq_lens.max() - # as input_dict may have extra zero-padding beyond seq_lens.max(). - # Use add_time_dimension to handle this - self.time_major = self.model_config.get("_time_major", False) - inputs = add_time_dimension( - flat_inputs, - seq_lens=seq_lens, - framework="torch", - time_major=self.time_major, - ) - lstm_out, new_state = self.forward_rnn(inputs, state, seq_lens) - lstm_out = torch.reshape(lstm_out, [-1, self.lstm_state_size]) - - value_input = torch.cat((cnn_out, lstm_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), new_state - - @override(TorchRNN) - def forward_rnn(self, inputs, state, seq_lens): - """Feeds `inputs` (B x T x ..) through the Gru Unit. - - Returns the resulting outputs as a sequence (B x T x ...). - Values are stored in self._cur_value in simple (B) shape (where B - contains both the B and T dims!). - - Returns: - NN Outputs (B x T x ...) as sequence. - The state batches as a List of two items (c- and h-states). - """ - linear_out = nn.functional.tanh(self.linear(inputs)) - - lstm_out, [h, c] = self.lstm(linear_out, [torch.unsqueeze(state[0], 0), torch.unsqueeze(state[1], 0)]) - - return lstm_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)] - - -def env_creator(_): - """ - Petting Zoo environment for search of shipwrecked people. - check it out at - https://github.com/pfeinsper/drone-swarm-search - or install with - pip install DSSE - """ - render_mode = "human" if args.see else "ansi" - env = DroneSwarmSearch( - drone_amount=4, - grid_size=40, - dispersion_inc=0.1, - person_initial_position=(20, 20), - render_mode=render_mode, - render_grid=True - ) - positions = [ - (20, 0), - (20, 39), - (0, 20), - (39, 20), - ] - env = AllPositionsWrapper(env) - env = RetainDronePosWrapper(env, positions) - return env - -env = env_creator(None) -register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) -ModelCatalog.register_custom_model("CNNModel", CNNModel) -ray.init() - - -checkpoint_path = args.checkpoint -PPOagent = PPO.from_checkpoint(checkpoint_path) - -reward_sum = 0 -i = 0 - -if args.see: - sample_model = PPOagent.get_policy().model - with PygameRecord("test_trained.gif", 5) as rec: - obs, info = env.reset() - reward_sum = 0 - state = sample_model.get_initial_state() - # state = init_hidden() - i = 0 - # done = False - while env.agents: - print(obs) - actions = {} - # for k, v in obs.items(): - # action, state, _ = PPOagent.compute_single_action(v, state, explore=False) - # actions[k] = action - actions = PPOagent.compute_actions(obs, state, explore=False) - obs, rw, term, trunc, info = env.step(actions) - # done = any(term.values()) or any(trunc.values()) - reward_sum += sum(rw.values()) - i += 1 - rec.add_frame() -else: - rewards = [] - actions_statics = [] - founds = 0 - N_EVALS = 200 - sample_model = PPOagent.get_policy().model - for _ in range(N_EVALS): - print(_) - obs, info = env.reset() - reward_sum = 0 - # state = init_hidden() - state = sample_model.get_initial_state() - i = 0 - while env.agents: - actions = {} - # for k, v in obs.items(): - # action, state, _ = PPOagent.compute_single_action(v, state, explore=False) - # actions[k] = action - actions = PPOagent.compute_actions(obs, state, explore=False) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - actions_statics.append(i) - rewards.append(reward_sum) - for _, v in info.items(): - if v["Found"]: - founds += 1 - break - -print("Average reward: ", sum(rewards) / N_EVALS) -print("Found %: ", founds / N_EVALS) -print("Mean steps: ", sum(actions_statics) / N_EVALS) -print("Median steps: ", np.median(actions_statics)) -print("Found: ", info) -env.close() \ No newline at end of file diff --git a/src/test_trained_cov.py b/src/test_trained_cov.py deleted file mode 100644 index 4706fce..0000000 --- a/src/test_trained_cov.py +++ /dev/null @@ -1,169 +0,0 @@ -import os -import pathlib -from recorder import PygameRecord -from DSSE import CoverageDroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -import ray -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env -from ray.rllib.algorithms.ppo import PPO -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -import torch.nn as nn -import argparse -import torch - - - -argparser = argparse.ArgumentParser() -argparser.add_argument("--checkpoint", type=str, required=True) -argparser.add_argument("--see", action="store_true", default=False) -args = argparser.parse_args() - - -class CNNModel(TorchModelV2, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - TorchModelV2.__init__( - self, obs_space, act_space, num_outputs, model_config, name, **kw - ) - nn.Module.__init__(self) - - first_cnn_out = (obs_space[1].shape[0] - 8) + 1 - second_cnn_out = (first_cnn_out - 4) + 1 - flatten_size = 32 * second_cnn_out * second_cnn_out - print("Cnn Dense layer input size: ", flatten_size) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(8, 8), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Sequential( - nn.Linear(obs_space[0].shape[0], 512), - nn.Tanh(), - nn.Linear(512, 256), - nn.Tanh(), - ) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - - def forward(self, input_dict, state, seq_lens): - input_positions = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - linear_out = self.linear(input_positions) - - value_input = torch.cat((cnn_out, linear_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), state - - def value_function(self): - return self._value_out.flatten() - -# Register the model -ModelCatalog.register_custom_model("CNNModel", CNNModel) - - -def env_creator(args): - print("-------------------------- ENV CREATOR --------------------------") - N_AGENTS = 8 - # 6 hours of simulation, 600 radius - env = CoverageDroneSwarmSearch( - timestep_limit=180, drone_amount=N_AGENTS, prob_matrix_path="presim_20.npy", render_mode="human" - ) - env = AllPositionsWrapper(env) - grid_size = env.grid_size - positions = position_on_diagonal(grid_size, N_AGENTS) - env = RetainDronePosWrapper(env, positions) - return env - -def position_on_diagonal(grid_size, drone_amount): - positions = [] - center = grid_size // 2 - for i in range(-drone_amount // 2, drone_amount // 2): - positions.append((center + i, center + i)) - return positions - -env = env_creator(None) -register_env("DSSE_Coverage", lambda config: ParallelPettingZooEnv(env_creator(config))) -ray.init() - - -checkpoint_path = args.checkpoint -PPOagent = PPO.from_checkpoint(checkpoint_path) - -reward_sum = 0 -i = 0 - -if args.see: - obs, info = env.reset() - with PygameRecord("test_trained.gif", 5) as rec: - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rec.add_frame() -else: - rewards = [] - founds = 0 - N_EVALS = 1000 - for _ in range(N_EVALS): - obs, info = env.reset() - reward_sum = 0 - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs, explore=False) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rewards.append(reward_sum) - for _, v in info.items(): - if v["Found"]: - founds += 1 - break - print("Average reward: ", sum(rewards) / N_EVALS) - print("Found %: ", founds / N_EVALS) - -print("Total reward: ", reward_sum) -print("Total steps: ", i) -print("Found: ", info) -env.close() diff --git a/src/test_trained_cov_mlp.py b/src/test_trained_cov_mlp.py deleted file mode 100644 index e8f7bb4..0000000 --- a/src/test_trained_cov_mlp.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -import pathlib -from recorder import PygameRecord -from DSSE import CoverageDroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper, AllFlattenWrapper -import ray -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env -from ray.rllib.algorithms.ppo import PPO -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -import torch.nn as nn -import argparse -import torch - - - -argparser = argparse.ArgumentParser() -argparser.add_argument("--checkpoint", type=str, required=True) -argparser.add_argument("--see", action="store_true", default=False) -args = argparser.parse_args() - - -def env_creator(_): - print("-------------------------- ENV CREATOR --------------------------") - N_AGENTS = 2 - render_mode = "human" if args.see else "ansi" - # 6 hours of simulation, 600 radius - env = CoverageDroneSwarmSearch( - timestep_limit=200, drone_amount=N_AGENTS, prob_matrix_path="min_matrix.npy", render_mode=render_mode - ) - env = AllFlattenWrapper(env) - grid_size = env.grid_size - print("Grid size: ", grid_size) - positions = [ - (0, grid_size // 2), - (grid_size - 1, grid_size // 2), - ] - env = RetainDronePosWrapper(env, positions) - return env -def position_on_diagonal(grid_size, drone_amount): - positions = [] - center = grid_size // 2 - for i in range(-drone_amount // 2, drone_amount // 2): - positions.append((center + i, center + i)) - return positions - -env = env_creator(None) -register_env("DSSE_Coverage", lambda config: ParallelPettingZooEnv(env_creator(config))) -ray.init() - -def print_mean(values, name): - print(f"Mean of {name}: ", sum(values) / len(values)) - -checkpoint_path = args.checkpoint -PPOagent = PPO.from_checkpoint(checkpoint_path) - -reward_sum = 0 -i = 0 - -if args.see: - obs, info = env.reset() - with PygameRecord("test_trained.gif", 5) as rec: - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rec.add_frame() - print(info) -else: - rewards = [] - cov_rate = [] - steps_needed = [] - repeated_cov = [] - - N_EVALS = 500 - for _ in range(N_EVALS): - i = 0 - obs, info = env.reset() - reward_sum = 0 - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rewards.append(reward_sum) - steps_needed.append(i) - cov_rate.append(info["drone0"]["coverage_rate"]) - repeated_cov.append(info["drone0"]["repeated_coverage"]) - - print_mean(rewards, "rewards") - print_mean(steps_needed, "steps needed") - print_mean(cov_rate, "coverage rate") - print_mean(repeated_cov, "repeated coverage") - -print("Total reward: ", reward_sum) -print("Total steps: ", i) -print("Found: ", info) -env.close() diff --git a/src/test_trained_search.py b/src/test_trained_search.py deleted file mode 100644 index e95431f..0000000 --- a/src/test_trained_search.py +++ /dev/null @@ -1,173 +0,0 @@ -from recorder import PygameRecord -from DSSE import DroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -from DSSE.environment.wrappers.communication_wrapper import CommunicationWrapper -import ray -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env -from ray.rllib.algorithms.ppo import PPO -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -import torch.nn as nn -import argparse -import torch -import numpy as np - - - -argparser = argparse.ArgumentParser() -argparser.add_argument("--checkpoint", type=str, required=True) -argparser.add_argument("--see", action="store_true", default=False) -args = argparser.parse_args() - - -class CNNModel(TorchModelV2, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - TorchModelV2.__init__( - self, obs_space, act_space, num_outputs, model_config, name, **kw - ) - nn.Module.__init__(self) - - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(8, 8), - stride=(1, 1), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Sequential( - nn.Linear(obs_space[0].shape[0], 512), - nn.Tanh(), - nn.Linear(512, 256), - nn.Tanh(), - ) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - - def forward(self, input_dict, state, seq_lens): - input_positions = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - linear_out = self.linear(input_positions) - - value_input = torch.cat((cnn_out, linear_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), state - - def value_function(self): - return self._value_out.flatten() - - - -# ModelCatalog.register_custom_model("MLPModel", MLPModel) -ModelCatalog.register_custom_model("CNNModel", CNNModel) - -# DEFINE HERE THE EXACT ENVIRONMENT YOU USED TO TRAIN THE AGENT -def env_creator(args): - env = DroneSwarmSearch( - drone_amount=4, - grid_size=40, - dispersion_inc=0.1, - person_initial_position=(20, 20), - ) - positions = [ - (20, 0), - (20, 39), - (0, 20), - (39, 20), - ] - env = AllPositionsWrapper(env) - env = CommunicationWrapper(env, n_steps=12) - env = RetainDronePosWrapper(env, positions) - return env - -env = env_creator(None) -register_env("DSSE", lambda config: ParallelPettingZooEnv(env_creator(config))) -ray.init() - - -checkpoint_path = args.checkpoint -PPOagent = PPO.from_checkpoint(checkpoint_path) - - -if args.see: - i = 0 - reward_sum = 0 - obs, info = env.reset() - with PygameRecord("test_trained.gif", 5) as rec: - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - # print(v) - # action = PPOagent.compute_actions(obs) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rec.add_frame() - print(info) - print(reward_sum) -else: - rewards = [] - actions_stat = [] - founds = 0 - N_EVALS = 5_000 - for epoch in range(N_EVALS): - print(epoch) - obs, info = env.reset() - i = 0 - reward_sum = 0 - while env.agents: - actions = {} - for k, v in obs.items(): - actions[k] = PPOagent.compute_single_action(v, explore=False) - obs, rw, term, trunc, info = env.step(actions) - reward_sum += sum(rw.values()) - i += 1 - rewards.append(reward_sum) - actions_stat.append(i) - - for _, v in info.items(): - if v["Found"]: - founds += 1 - break - print("Average reward: ", sum(rewards) / N_EVALS) - print("Average Actions: ", sum(actions_stat) / N_EVALS) - print("Median of actions: ", np.median(actions_stat)) - print("Found %: ", founds / N_EVALS) - -env.close() diff --git a/src/train_ppo_cnn.py b/src/train_ppo_cnn.py deleted file mode 100644 index cacab4b..0000000 --- a/src/train_ppo_cnn.py +++ /dev/null @@ -1,148 +0,0 @@ -import os -import pathlib -from DSSE import DroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -import ray -from ray import tune -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -from ray.tune.registry import register_env -from torch import nn -import torch - - -class CNNModel(TorchModelV2, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - TorchModelV2.__init__( - self, obs_space, act_space, num_outputs, model_config, name, **kw - ) - nn.Module.__init__(self) - - flatten_size = 32 * (obs_space[1].shape[0] - 7 - 3) * (obs_space[1].shape[1] - 7 - 3) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(8, 8), - stride=(1, 1), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(4, 4), - stride=(1, 1), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Sequential( - nn.Linear(obs_space[0].shape[0], 512), - nn.Tanh(), - nn.Linear(512, 256), - nn.Tanh(), - ) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - - def forward(self, input_dict, state, seq_lens): - input_positions = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - linear_out = self.linear(input_positions) - - value_input = torch.cat((cnn_out, linear_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), state - - def value_function(self): - return self._value_out.flatten() - - -def env_creator(args): - env = DroneSwarmSearch( - drone_amount=4, - grid_size=40, - dispersion_inc=0.1, - person_initial_position=(20, 20), - ) - positions = [ - (20, 0), - (20, 39), - (0, 20), - (39, 20), - ] - env = AllPositionsWrapper(env) - env = RetainDronePosWrapper(env, positions) - return env - - -if __name__ == "__main__": - ray.init() - - env_name = "DSSE" - - register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) - ModelCatalog.register_custom_model("CNNModel", CNNModel) - - config = ( - PPOConfig() - .environment(env=env_name) - .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") - .training( - train_batch_size=8192, - lr=1e-5, - gamma=0.9999999, - lambda_=0.9, - use_gae=True, - # clip_param=0.3, - # grad_clip=None, - entropy_coeff=0.01, - # vf_loss_coeff=0.25, - # vf_clip_param=10, - sgd_minibatch_size=300, - num_sgd_iter=10, - model={ - "custom_model": "CNNModel", - "_disable_preprocessor_api": True, - }, - ) - .experimental(_disable_preprocessor_api=True) - .debugging(log_level="ERROR") - .framework(framework="torch") - .resources(num_gpus=1) - ) - - curr_path = pathlib.Path().resolve() - tune.run( - "PPO", - name="PPO", - stop={"timesteps_total": 20_000_000 if not os.environ.get("CI") else 50000, "episode_reward_mean": 1.75}, - checkpoint_freq=10, - storage_path=f"{curr_path}/ray_res/" + env_name, - config=config.to_dict(), - ) diff --git a/src/train_ppo_cnn_cov.py b/src/train_ppo_cnn_cov.py deleted file mode 100644 index c988602..0000000 --- a/src/train_ppo_cnn_cov.py +++ /dev/null @@ -1,167 +0,0 @@ -import pathlib -from DSSE import CoverageDroneSwarmSearch -from DSSE.environment.wrappers import RetainDronePosWrapper, AllPositionsWrapper -import ray -from ray import tune -from ray.rllib.algorithms.ppo import PPOConfig -from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv -from ray.rllib.models import ModelCatalog -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -from ray.tune.registry import register_env -from torch import nn -import torch -import numpy as np - - -class CNNModel(TorchModelV2, nn.Module): - def __init__( - self, - obs_space, - act_space, - num_outputs, - model_config, - name, - **kw, - ): - print("OBSSPACE: ", obs_space) - TorchModelV2.__init__( - self, obs_space, act_space, num_outputs, model_config, name, **kw - ) - nn.Module.__init__(self) - - first_cnn_out = (obs_space[1].shape[0] - 3) + 1 - second_cnn_out = (first_cnn_out - 2) + 1 - flatten_size = 32 * second_cnn_out * second_cnn_out - print("Cnn Dense layer input size: ", flatten_size) - self.cnn = nn.Sequential( - nn.Conv2d( - in_channels=1, - out_channels=16, - kernel_size=(3, 3), - ), - nn.Tanh(), - nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=(2, 2), - ), - nn.Tanh(), - nn.Flatten(), - nn.Linear(flatten_size, 256), - nn.Tanh(), - ) - - self.linear = nn.Sequential( - nn.Linear(obs_space[0].shape[0], 512), - nn.Tanh(), - nn.Linear(512, 256), - nn.Tanh(), - ) - - self.join = nn.Sequential( - nn.Linear(256 * 2, 256), - nn.Tanh(), - ) - - self.policy_fn = nn.Linear(256, num_outputs) - self.value_fn = nn.Linear(256, 1) - - def forward(self, input_dict, state, seq_lens): - input_positions = input_dict["obs"][0].float() - input_matrix = input_dict["obs"][1].float() - - input_matrix = input_matrix.unsqueeze(1) - cnn_out = self.cnn(input_matrix) - linear_out = self.linear(input_positions) - - value_input = torch.cat((cnn_out, linear_out), dim=1) - value_input = self.join(value_input) - - self._value_out = self.value_fn(value_input) - return self.policy_fn(value_input), state - - def value_function(self): - return self._value_out.flatten() - -def env_creator(args): - print("-------------------------- ENV CREATOR --------------------------") - N_AGENTS = 2 - # 6 hours of simulation, 600 radius - env = CoverageDroneSwarmSearch( - timestep_limit=200, drone_amount=N_AGENTS, prob_matrix_path="min_matrix.npy" - ) - env = AllPositionsWrapper(env) - grid_size = env.grid_size - # positions = position_on_diagonal(grid_size, N_AGENTS) - # positions = position_on_circle(grid_size, N_AGENTS, 2) - positions = [ - (grid_size - 1, grid_size // 2), - (0, grid_size // 2), - ] - env = RetainDronePosWrapper(env, positions) - return env - -def position_on_diagonal(grid_size, drone_amount): - positions = [] - center = grid_size // 2 - for i in range(-drone_amount // 2, drone_amount // 2): - positions.append((center + i, center + i)) - return positions - -def position_on_circle(grid_size, drone_amount, radius): - positions = [] - center = grid_size // 2 - angle_increment = 2 * np.pi / drone_amount - - for i in range(drone_amount): - angle = i * angle_increment - x = center + int(radius * np.cos(angle)) - y = center + int(radius * np.sin(angle)) - positions.append((x, y)) - - return positions - - -if __name__ == "__main__": - ray.init() - - env_name = "DSSE_Coverage" - - register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config))) - ModelCatalog.register_custom_model("CNNModel", CNNModel) - - config = ( - PPOConfig() - .environment(env=env_name) - .rollouts(num_rollout_workers=6, rollout_fragment_length="auto") - .training( - train_batch_size=8192 * 5, - lr=6e-6, - gamma=0.9999999, - lambda_=0.9, - use_gae=True, - entropy_coeff=0.01, - vf_clip_param=100000, - sgd_minibatch_size=300, - num_sgd_iter=10, - model={ - "custom_model": "CNNModel", - "_disable_preprocessor_api": True, - }, - ) - .experimental(_disable_preprocessor_api=True) - .debugging(log_level="ERROR") - .framework(framework="torch") - .resources(num_gpus=1) - ) - - curr_path = pathlib.Path().resolve() - tune.run( - "PPO", - name="PPO_" + input("Exp name: "), - # resume=True, - stop={"timesteps_total": 20_000_000}, - checkpoint_freq=20, - storage_path=f"{curr_path}/ray_res/" + env_name, - config=config.to_dict(), - ) diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/utils/play_env.py b/src/utils/play_env.py new file mode 100644 index 0000000..e2276df --- /dev/null +++ b/src/utils/play_env.py @@ -0,0 +1,114 @@ +from .recorder import PygameRecord +import numpy as np +import pandas as pd + + +def print_mean(values, name): + print(f"Mean of {name}: ", sum(values) / len(values)) + + +def play_with_record(env, agent): + reward_sum = 0 + obs, info = env.reset() + with PygameRecord("test_trained.gif", 5) as rec: + while env.agents: + actions = {} + for k, v in obs.items(): + actions[k] = agent.compute_single_action(v, explore=False) + obs, rw, _, _, info = env.step(actions) + reward_sum += sum(rw.values()) + rec.add_frame() + print(info) + print(reward_sum) + + +def evaluate_agent_search(env, agent, n_evals=5000): + rewards = [] + actions_stat = [] + founds = 0 + for _ in range(n_evals): + obs, info = env.reset() + actions = 0 + reward_sum = 0 + while env.agents: + actions = {} + for k, v in obs.items(): + actions[k] = agent.compute_single_action(v, explore=False) + obs, rw, _, _, info = env.step(actions) + reward_sum += sum(rw.values()) + actions += 1 + rewards.append(reward_sum) + actions_stat.append(actions) + founds += any(info.values(), key=lambda x: x["Found"]) + + print_mean(rewards, "rewards") + print_mean(actions_stat, "steps needed") + print("Median of actions: ", np.median(actions_stat)) + print("Found %: ", founds / n_evals) + + +def evaluate_agent_coverage(env, agent, n_evals=5): + rewards = [] + cov_rate = [] + steps_needed = [] + repeated_cov = [] + cumulative_metrics = pd.DataFrame() + + for _ in range(n_evals): + steps = 0 + reward_sum = 0 + cumm_pos = [] + dynamic_cov_rate = [] + dynamic_repeated_cov = [] + + obs, info = env.reset() + while env.agents: + actions = {} + for k, v in obs.items(): + actions[k] = agent.compute_single_action(v, explore=False) + obs, rw, _, _, info = env.step(actions) + reward_sum += sum(rw.values()) + steps += 1 + cumm_pos.append(info["drone0"]["accumulated_pos"]) + dynamic_cov_rate.append(info["drone0"]["coverage_rate"]) + dynamic_repeated_cov.append(info["drone0"]["repeated_coverage"]) + + cumulative_metrics = pd.concat( + [ + cumulative_metrics, + pd.DataFrame( + { + "accumulated_pos": cumm_pos, + "step": range(len(cumm_pos)), + "coverage_rate": dynamic_cov_rate, + "repeated_coverage": dynamic_repeated_cov, + "algorithm": "PPO", + } + ), + ], + ignore_index=True, + ) + rewards.append(reward_sum) + steps_needed.append(steps) + cov_rate.append(info["drone0"]["coverage_rate"]) + repeated_cov.append(info["drone0"]["repeated_coverage"]) + + print_mean(rewards, "rewards") + print_mean(steps_needed, "steps needed") + print_mean(cov_rate, "coverage rate") + print_mean(repeated_cov, "repeated coverage") + df = pd.DataFrame( + { + "rewards": rewards, + "steps_needed": steps_needed, + "coverage_rate": cov_rate, + "repeated_coverage": repeated_cov, + } + ) + df.to_csv("coverage_results.csv", index=False) + cumulative_metrics.to_csv("cumm_metrics.csv", index=False) + + print(f"Total reward: {reward_sum}") + print(f"Total steps: {steps}") + print("Info: ", info) + env.close() diff --git a/src/utils/random_position_wrapper.py b/src/utils/random_position_wrapper.py new file mode 100644 index 0000000..a0c5974 --- /dev/null +++ b/src/utils/random_position_wrapper.py @@ -0,0 +1,41 @@ +from pettingzoo.utils.wrappers import BaseParallelWrapper +from DSSE import DroneSwarmSearch +import random + + +class RandomPositionWrapper(BaseParallelWrapper): + """ + Wrapper that modifies the reset function to randomize the positions of the drones + """ + + def __init__(self, env: DroneSwarmSearch): + super().__init__(env) + self.possible_positions = [ + (x, y) for x in range(self.env.grid_size) for y in range(self.env.grid_size) + ] + + def reset(self, **kwargs): + opt = kwargs.get("options", {}) + # Generate random positions for the drones + opt["drones_positions"] = random.sample( + self.possible_positions, len(self.env.possible_agents) + ) + kwargs["options"] = opt + + return self.env.reset(**kwargs) + + +if __name__ == "__main__": + env = DroneSwarmSearch( + grid_size=9, + timestep_limit=10, + render_mode="human", + render_grid=True, + drone_amount=4, + ) + env = RandomPositionWrapper(env) + for _ in range(5): + env.reset() + while env.agents: + env.step({agent: env.action_space(agent).sample() for agent in env.agents}) + env.close() diff --git a/src/recorder.py b/src/utils/recorder.py similarity index 99% rename from src/recorder.py rename to src/utils/recorder.py index 30c0240..253fa28 100644 --- a/src/recorder.py +++ b/src/utils/recorder.py @@ -85,4 +85,4 @@ def __exit__(self, exc_type, exc_value, traceback): if n_frames == 0: break recorder.save() - pygame.quit() \ No newline at end of file + pygame.quit() diff --git a/uv.lock b/uv.lock new 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