fix(pu): fix longrun performance of muzero in mspacman and qbert

lzero/entry/__init__.py

@@ -5,9 +5,15 @@
 from .train_alphazero import train_alphazero
 from .train_muzero import train_muzero
 from .train_muzero_segment import train_muzero_segment
+from .train_muzero_segment_save_buffer import train_muzero_segment_save_buffer
+from .train_muzero_segment_save_buffer_from_ckpt import train_muzero_segment_save_buffer_from_ckpt
+
+
 from .train_muzero_with_gym_env import train_muzero_with_gym_env
 from .train_muzero_with_reward_model import train_muzero_with_reward_model
 from .train_rezero import train_rezero
 from .train_unizero import train_unizero
 from .train_unizero_segment import train_unizero_segment
+from .train_unizero_segment_from_buffer import train_unizero_segment_from_buffer
+
 from .utils import *

lzero/entry/train_muzero_segment.py

@@ -24,6 +24,8 @@
 timer = EasyTimer()
 
 
+
+
 def train_muzero_segment(
         input_cfg: Tuple[dict, dict],
         seed: int = 0,
@@ -88,7 +90,9 @@ def train_muzero_segment(
 
     # load pretrained model
     if model_path is not None:
+        logging.info(f'Loading model from {model_path} begin...')
         policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
+        logging.info(f'Loading model from {model_path} end!')
 
     # Create worker components: learner, collector, evaluator, replay buffer, commander.
     tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) if get_rank() == 0 else None
@@ -144,6 +148,8 @@ def train_muzero_segment(
     train_epoch = 0
     reanalyze_batch_size = cfg.policy.reanalyze_batch_size
 
+
+
     while True:
         log_buffer_memory_usage(learner.train_iter, replay_buffer, tb_logger)
         log_buffer_run_time(learner.train_iter, replay_buffer, tb_logger)
@@ -169,7 +175,9 @@ def train_muzero_segment(
             collect_kwargs['epsilon'] = 0.0
 
         # Evaluate policy performance.
-        if evaluator.should_eval(learner.train_iter):
+        if learner.train_iter==0 or evaluator.should_eval(learner.train_iter):
+        # if evaluator.should_eval(learner.train_iter):
+
             if cfg.policy.eval_offline:
                 eval_train_iter_list.append(learner.train_iter)
                 eval_train_envstep_list.append(collector.envstep)

lzero/entry/train_muzero_segment_orig.py

@@ -0,0 +1,260 @@
+import logging
+import os
+from functools import partial
+from typing import Optional, Tuple
+
+import torch
+from ding.config import compile_config
+from ding.envs import create_env_manager
+from ding.envs import get_vec_env_setting
+from ding.policy import create_policy
+from ding.rl_utils import get_epsilon_greedy_fn
+from ding.utils import EasyTimer
+from ding.utils import set_pkg_seed, get_rank
+from ding.worker import BaseLearner
+from tensorboardX import SummaryWriter
+
+from lzero.entry.utils import log_buffer_memory_usage, log_buffer_run_time
+from lzero.policy import visit_count_temperature
+from lzero.policy.random_policy import LightZeroRandomPolicy
+from lzero.worker import MuZeroEvaluator as Evaluator
+from lzero.worker import MuZeroSegmentCollector as Collector
+from .utils import random_collect, calculate_update_per_collect
+
+timer = EasyTimer()
+
+
+
+
+def train_muzero_segment(
+        input_cfg: Tuple[dict, dict],
+        seed: int = 0,
+        model: Optional[torch.nn.Module] = None,
+        model_path: Optional[str] = None,
+        max_train_iter: Optional[int] = int(1e10),
+        max_env_step: Optional[int] = int(1e10),
+) -> 'Policy':  # noqa
+    """
+    Overview:
+        The train entry for MCTS+RL algorithms (with muzero_segment_collector and buffer reanalyze trick), including MuZero, EfficientZero, Sampled MuZero, Sampled EfficientZero, Gumbel MuZero, StochasticMuZero.
+    Arguments:
+        - input_cfg (:obj:`Tuple[dict, dict]`): Config in dict type.
+            ``Tuple[dict, dict]`` type means [user_config, create_cfg].
+        - seed (:obj:`int`): Random seed.
+        - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
+        - model_path (:obj:`Optional[str]`): The pretrained model path, which should
+            point to the ckpt file of the pretrained model, and an absolute path is recommended.
+            In LightZero, the path is usually something like ``exp_name/ckpt/ckpt_best.pth.tar``.
+        - max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training.
+        - max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps.
+    Returns:
+        - policy (:obj:`Policy`): Converged policy.
+    """
+
+    cfg, create_cfg = input_cfg
+    assert create_cfg.policy.type in ['efficientzero', 'muzero', 'muzero_context', 'muzero_rnn_full_obs', 'sampled_efficientzero', 'sampled_muzero', 'gumbel_muzero', 'stochastic_muzero'], \
+        "train_muzero entry now only support the following algo.: 'efficientzero', 'muzero', 'sampled_efficientzero', 'gumbel_muzero', 'stochastic_muzero'"
+
+    if create_cfg.policy.type in ['muzero', 'muzero_context', 'muzero_rnn_full_obs']:
+        from lzero.mcts import MuZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'efficientzero':
+        from lzero.mcts import EfficientZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'sampled_efficientzero':
+        from lzero.mcts import SampledEfficientZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'sampled_muzero':
+        from lzero.mcts import SampledMuZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'gumbel_muzero':
+        from lzero.mcts import GumbelMuZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'stochastic_muzero':
+        from lzero.mcts import StochasticMuZeroGameBuffer as GameBuffer
+
+    if cfg.policy.cuda and torch.cuda.is_available():
+        cfg.policy.device = 'cuda'
+    else:
+        cfg.policy.device = 'cpu'
+
+    cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True)
+    # Create main components: env, policy
+    env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
+    collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
+    evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
+
+    collector_env.seed(cfg.seed)
+    evaluator_env.seed(cfg.seed, dynamic_seed=False)
+    set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+
+    if cfg.policy.eval_offline:
+        cfg.policy.learn.learner.hook.save_ckpt_after_iter = cfg.policy.eval_freq
+
+    policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval'])
+
+    # load pretrained model
+    if model_path is not None:
+        policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
+
+    # Create worker components: learner, collector, evaluator, replay buffer, commander.
+    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) if get_rank() == 0 else None
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+
+    # ==============================================================
+    # MCTS+RL algorithms related core code
+    # ==============================================================
+    policy_config = cfg.policy
+    batch_size = policy_config.batch_size
+    # specific game buffer for MCTS+RL algorithms
+    replay_buffer = GameBuffer(policy_config)
+    collector = Collector(
+        env=collector_env,
+        policy=policy.collect_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+        policy_config=policy_config
+    )
+    evaluator = Evaluator(
+        eval_freq=cfg.policy.eval_freq,
+        n_evaluator_episode=cfg.env.n_evaluator_episode,
+        stop_value=cfg.env.stop_value,
+        env=evaluator_env,
+        policy=policy.eval_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+        policy_config=policy_config
+    )
+
+    # ==============================================================
+    # Main loop
+    # ==============================================================
+    # Learner's before_run hook.
+    learner.call_hook('before_run')
+
+    if cfg.policy.update_per_collect is not None:
+        update_per_collect = cfg.policy.update_per_collect
+
+    # The purpose of collecting random data before training:
+    # Exploration: Collecting random data helps the agent explore the environment and avoid getting stuck in a suboptimal policy prematurely.
+    # Comparison: By observing the agent's performance during random action-taking, we can establish a baseline to evaluate the effectiveness of reinforcement learning algorithms.
+    if cfg.policy.random_collect_episode_num > 0:
+        random_collect(cfg.policy, policy, LightZeroRandomPolicy, collector, collector_env, replay_buffer)
+    if cfg.policy.eval_offline:
+        eval_train_iter_list = []
+        eval_train_envstep_list = []
+
+    # Evaluate the random agent
+    # stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+
+    buffer_reanalyze_count = 0
+    train_epoch = 0
+    reanalyze_batch_size = cfg.policy.reanalyze_batch_size
+
+
+
+    while True:
+        log_buffer_memory_usage(learner.train_iter, replay_buffer, tb_logger)
+        log_buffer_run_time(learner.train_iter, replay_buffer, tb_logger)
+        collect_kwargs = {}
+        # set temperature for visit count distributions according to the train_iter,
+        # please refer to Appendix D in MuZero paper for details.
+        collect_kwargs['temperature'] = visit_count_temperature(
+            policy_config.manual_temperature_decay,
+            policy_config.fixed_temperature_value,
+            policy_config.threshold_training_steps_for_final_temperature,
+            trained_steps=learner.train_iter
+        )
+
+        if policy_config.eps.eps_greedy_exploration_in_collect:
+            epsilon_greedy_fn = get_epsilon_greedy_fn(
+                start=policy_config.eps.start,
+                end=policy_config.eps.end,
+                decay=policy_config.eps.decay,
+                type_=policy_config.eps.type
+            )
+            collect_kwargs['epsilon'] = epsilon_greedy_fn(collector.envstep)
+        else:
+            collect_kwargs['epsilon'] = 0.0
+
+        # Evaluate policy performance.
+        if evaluator.should_eval(learner.train_iter):
+            if cfg.policy.eval_offline:
+                eval_train_iter_list.append(learner.train_iter)
+                eval_train_envstep_list.append(collector.envstep)
+            else:
+                stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+                if stop:
+                    break
+
+        # Collect data by default config n_sample/n_episode.
+        new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)
+
+        # Determine updates per collection
+        update_per_collect = calculate_update_per_collect(cfg, new_data)
+
+        # save returned new_data collected by the collector
+        replay_buffer.push_game_segments(new_data)
+        # remove the oldest data if the replay buffer is full.
+        replay_buffer.remove_oldest_data_to_fit()
+
+        # Periodically reanalyze buffer
+        if cfg.policy.buffer_reanalyze_freq >= 1:
+            # Reanalyze buffer <buffer_reanalyze_freq> times in one train_epoch
+            reanalyze_interval = update_per_collect // cfg.policy.buffer_reanalyze_freq
+        else:
+            # Reanalyze buffer each <1/buffer_reanalyze_freq> train_epoch
+            if train_epoch % (1//cfg.policy.buffer_reanalyze_freq) == 0 and replay_buffer.get_num_of_transitions()//cfg.policy.num_unroll_steps > int(reanalyze_batch_size/cfg.policy.reanalyze_partition):
+                with timer:
+                    # Each reanalyze process will reanalyze <reanalyze_batch_size> sequences (<cfg.policy.num_unroll_steps> transitions per sequence)
+                    replay_buffer.reanalyze_buffer(reanalyze_batch_size, policy)
+                buffer_reanalyze_count += 1
+                logging.info(f'Buffer reanalyze count: {buffer_reanalyze_count}')
+                logging.info(f'Buffer reanalyze time: {timer.value}')
+
+        # Learn policy from collected data.
+        for i in range(update_per_collect):
+
+            if cfg.policy.buffer_reanalyze_freq >= 1:
+                # Reanalyze buffer <buffer_reanalyze_freq> times in one train_epoch
+                if i % reanalyze_interval == 0 and replay_buffer.get_num_of_transitions() // cfg.policy.num_unroll_steps > int(
+                        reanalyze_batch_size / cfg.policy.reanalyze_partition):
+                    # Each reanalyze process will reanalyze <reanalyze_batch_size> sequences (<cfg.policy.num_unroll_steps> transitions per sequence)
+                    replay_buffer.reanalyze_buffer(reanalyze_batch_size, policy)
+                    buffer_reanalyze_count += 1
+                    logging.info(f'Buffer reanalyze count: {buffer_reanalyze_count}')
+
+            # Learner will train ``update_per_collect`` times in one iteration.
+            if replay_buffer.get_num_of_transitions() > batch_size:
+                train_data = replay_buffer.sample(batch_size, policy)
+            else:
+                logging.warning(
+                    f'The data in replay_buffer is not sufficient to sample a mini-batch: '
+                    f'batch_size: {batch_size}, '
+                    f'{replay_buffer} '
+                    f'continue to collect now ....'
+                )
+                break
+
+            # The core train steps for MCTS+RL algorithms.
+            log_vars = learner.train(train_data, collector.envstep)
+
+            if cfg.policy.use_priority:
+                replay_buffer.update_priority(train_data, log_vars[0]['value_priority_orig'])
+
+        train_epoch += 1
+
+        if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
+            if cfg.policy.eval_offline:
+                logging.info(f'eval offline beginning...')
+                ckpt_dirname = './{}/ckpt'.format(learner.exp_name)
+                # Evaluate the performance of the pretrained model.
+                for train_iter, collector_envstep in zip(eval_train_iter_list, eval_train_envstep_list):
+                    ckpt_name = 'iteration_{}.pth.tar'.format(train_iter)
+                    ckpt_path = os.path.join(ckpt_dirname, ckpt_name)
+                    # load the ckpt of pretrained model
+                    policy.learn_mode.load_state_dict(torch.load(ckpt_path, map_location=cfg.policy.device))
+                    stop, reward = evaluator.eval(learner.save_checkpoint, train_iter, collector_envstep)
+                    logging.info(
+                        f'eval offline at train_iter: {train_iter}, collector_envstep: {collector_envstep}, reward: {reward}')
+                logging.info(f'eval offline finished!')
+            break
+
+    # Learner's after_run hook.
+    learner.call_hook('after_run')
+    return policy