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ReykCSReykCS
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Merge pull request #9 from Arena-Rosnav/master
Master
2 parents 1af3a40 + 7669886 commit c11ad89

20 files changed

+406
-203
lines changed

‎agents/jackal_marl/best_model.zip‎

18 MB
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‎agents/jackal_marl/hyperparameters.json‎

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{
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"agent_name": "AGENT_24_2022_02_04__11_28",
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"robot": "jackal",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_06",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
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"train_max_steps_per_episode": 400,
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"eval_max_steps_per_episode": 1100,
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"goal_radius": 0.7,
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"curr_stage": 1,
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"batch_size": 38400,
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"gamma": 0.99,
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"n_steps": 3200,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
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"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22,
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"observation_space": ["laser_scan", "goal_in_robot_frame", "last_action"]
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}

‎agents/new_jackal/hyperparameters.json‎

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@@ -1,24 +1,25 @@
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{
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"agent_name": "AGENT_24_2022_02_04__11_28",
3-
"robot": "jackal",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_05",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
9-
"train_max_steps_per_episode": 150,
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"eval_max_steps_per_episode": 200,
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"goal_radius": 0.7,
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"curr_stage": 8,
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"batch_size": 19200,
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"gamma": 0.99,
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"n_steps": 4800,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
20-
"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22
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}
2+
"agent_name": "AGENT_24_2022_02_04__11_28",
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"robot": "jackal",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_05",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
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"train_max_steps_per_episode": 150,
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"eval_max_steps_per_episode": 200,
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"goal_radius": 0.7,
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"curr_stage": 8,
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"batch_size": 19200,
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"gamma": 0.99,
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"n_steps": 4800,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
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"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22,
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"observation_space": ["laser_scan", "goal_in_robot_frame", "last_action"]
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}

‎agents/ridgeback_marl/best_model.zip‎

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‎agents/ridgeback_marl/hyperparameters.json‎

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{
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"agent_name": "ridgeback_2022_09_20__15_37",
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"robot": "myrobot",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_06",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
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"train_max_steps_per_episode": 800,
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"eval_max_steps_per_episode": 1100,
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"goal_radius": 0.7,
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"curr_stage": 1,
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"batch_size": 57600,
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"gamma": 0.99,
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"n_steps": 4800,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
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"gae_lambda": 0.95,
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"m_batch_size": 16,
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"n_epochs": 3,
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"clip_range": 0.22,
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"observation_space": ["laser_scan", "goal_in_robot_frame", "last_action"]
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}

‎agents/rto_tlabs/hyperparameters.json‎

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{
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"agent_name": "AGENT_24_2022_02_04__11_36",
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"robot": "rto_real",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_05",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
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"train_max_steps_per_episode": 120,
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"eval_max_steps_per_episode": 170,
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"goal_radius": 0.7,
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"curr_stage": 10,
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"batch_size": 19200,
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"gamma": 0.99,
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"n_steps": 2400,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
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"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22
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}
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"agent_name": "AGENT_24_2022_02_04__11_36",
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"robot": "rto_real",
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"actions_in_observationspace": true,
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"reward_fnc": "rule_05",
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"discrete_action_space": false,
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"normalize": false,
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"task_mode": "staged",
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"train_max_steps_per_episode": 120,
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"eval_max_steps_per_episode": 170,
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"goal_radius": 0.7,
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"curr_stage": 10,
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"batch_size": 19200,
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"gamma": 0.99,
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"n_steps": 2400,
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"ent_coef": 0.005,
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"learning_rate": 0.0003,
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"vf_coef": 0.22,
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"max_grad_norm": 0.5,
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"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22,
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"observation_space": ["laser_scan", "goal_in_robot_frame", "last_action"]
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}

‎agents/rto_tlabs_marl/best_model.zip‎

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‎agents/rto_tlabs_marl/hyperparameters.json‎

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{
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"agent_name": "AGENT_24_2022_02_04__11_36",
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"robot": "rto_real",
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"actions_in_observationspace": true,
5+
"reward_fnc": "rule_06",
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"discrete_action_space": false,
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"normalize": false,
8+
"task_mode": "staged",
9+
"train_max_steps_per_episode": 400,
10+
"eval_max_steps_per_episode": 1100,
11+
"goal_radius": 0.7,
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"curr_stage": 1,
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"batch_size": 38400,
14+
"gamma": 0.99,
15+
"n_steps": 3200,
16+
"ent_coef": 0.005,
17+
"learning_rate": 0.0003,
18+
"vf_coef": 0.22,
19+
"max_grad_norm": 0.5,
20+
"gae_lambda": 0.95,
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"m_batch_size": 15,
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"n_epochs": 3,
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"clip_range": 0.22,
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"observation_space": ["laser_scan", "goal_in_robot_frame", "last_action"]
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}

‎rosnav/model/agent_factory.py‎

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@@ -40,21 +40,23 @@ def inner_wrapper(wrapped_class) -> Callable:
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# end register()
4141

4242
@classmethod
43-
def instantiate(cls, name: str, **kwargs) -> Union[Type[BaseAgent], Type[BasePolicy]]:
43+
def instantiate(
44+
cls, name: str, **kwargs
45+
) -> Union[Type[BaseAgent], Type[BasePolicy]]:
4446
"""Factory command to create the agent.
4547
This method gets the appropriate agent class from the registry
4648
and creates an instance of it, while passing in the parameters
4749
given in ``kwargs``.
4850
4951
Args:
50-
name (str): The name of the agent to create.
52+
name (str): The name of the agent to create.agent_class
5153
5254
Returns:
5355
An instance of the agent that is created.
5456
"""
5557
assert name in cls.registry, f"Agent '{name}' is not registered!"
5658
agent_class = cls.registry[name]
57-
59+
5860
if issubclass(agent_class, BaseAgent):
5961
return agent_class(**kwargs)
6062
else:

‎rosnav/model/base_agent.py‎

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from typing import Type, List
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from abc import ABC, abstractmethod
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from enum import Enum
5-
from torch.nn.modules.module import Module
3+
from typing import List, Type
4+
65
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
6+
from torch.nn.modules.module import Module
77

88

99
class PolicyType(Enum):
@@ -47,14 +47,17 @@ def activation_fn(self) -> Type[Module]:
4747
pass
4848

4949
def get_kwargs(self):
50+
fe_kwargs = self.features_extractor_kwargs
51+
fe_kwargs["robot_model"] = self.robot_model
52+
5053
kwargs = {
5154
"features_extractor_class": self.features_extractor_class,
52-
"features_extractor_kwargs": self.features_extractor_kwargs,
55+
"features_extractor_kwargs": fe_kwargs,
5356
"net_arch": self.net_arch,
5457
"activation_fn": self.activation_fn,
5558
}
56-
if not kwargs['features_extractor_class']:
57-
del kwargs['features_extractor_class']
58-
if not kwargs['features_extractor_kwargs']:
59-
del kwargs['features_extractor_kwargs']
59+
if not kwargs["features_extractor_class"]:
60+
del kwargs["features_extractor_class"]
61+
if not kwargs["features_extractor_kwargs"]:
62+
del kwargs["features_extractor_kwargs"]
6063
return kwargs

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