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| import os | |
| import gym | |
| from tensorboardX import SummaryWriter | |
| from easydict import EasyDict | |
| from copy import deepcopy | |
| from functools import partial | |
| from ding.config import compile_config | |
| from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer | |
| from ding.envs import SyncSubprocessEnvManager | |
| from ding.policy import PPGPolicy | |
| from ding.model import PPG | |
| from ding.utils import set_pkg_seed, deep_merge_dicts | |
| from dizoo.atari.envs import AtariEnv | |
| from dizoo.atari.config.serial.spaceinvaders.spaceinvaders_ppg_config import spaceinvaders_ppg_config | |
| def main(cfg, seed=0, max_iterations=int(1e10)): | |
| cfg.exp_name = 'spaceinvaders_ppg_seed0' | |
| cfg = compile_config( | |
| cfg, | |
| SyncSubprocessEnvManager, | |
| PPGPolicy, | |
| BaseLearner, | |
| SampleSerialCollector, | |
| InteractionSerialEvaluator, { | |
| 'policy': AdvancedReplayBuffer, | |
| 'value': AdvancedReplayBuffer | |
| }, | |
| save_cfg=True | |
| ) | |
| collector_env_cfg = AtariEnv.create_collector_env_cfg(cfg.env) | |
| evaluator_env_cfg = AtariEnv.create_evaluator_env_cfg(cfg.env) | |
| collector_env = SyncSubprocessEnvManager( | |
| env_fn=[partial(AtariEnv, cfg=c) for c in collector_env_cfg], cfg=cfg.env.manager | |
| ) | |
| evaluator_env = SyncSubprocessEnvManager( | |
| env_fn=[partial(AtariEnv, cfg=c) for c in evaluator_env_cfg], cfg=cfg.env.manager | |
| ) | |
| collector_env.seed(seed) | |
| evaluator_env.seed(seed, dynamic_seed=False) | |
| set_pkg_seed(seed, use_cuda=cfg.policy.cuda) | |
| model = PPG(**cfg.policy.model) | |
| policy = PPGPolicy(cfg.policy, model=model) | |
| tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) | |
| learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) | |
| collector = SampleSerialCollector( | |
| cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| evaluator = InteractionSerialEvaluator( | |
| cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| policy_buffer = AdvancedReplayBuffer( | |
| cfg.policy.other.replay_buffer.policy, tb_logger, exp_name=cfg.exp_name, instance_name='policy_buffer' | |
| ) | |
| value_buffer = AdvancedReplayBuffer( | |
| cfg.policy.other.replay_buffer.value, tb_logger, exp_name=cfg.exp_name, instance_name='value_buffer' | |
| ) | |
| while True: | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) | |
| if stop: | |
| break | |
| new_data = collector.collect(train_iter=learner.train_iter) | |
| policy_buffer.push(new_data, cur_collector_envstep=collector.envstep) | |
| value_buffer.push(deepcopy(new_data), cur_collector_envstep=collector.envstep) | |
| for i in range(cfg.policy.learn.update_per_collect): | |
| batch_size = learner.policy.get_attribute('batch_size') | |
| policy_data = policy_buffer.sample(batch_size['policy'], learner.train_iter) | |
| value_data = value_buffer.sample(batch_size['value'], learner.train_iter) | |
| if policy_data is not None and value_data is not None: | |
| train_data = {'policy': policy_data, 'value': value_data} | |
| learner.train(train_data, collector.envstep) | |
| policy_buffer.clear() | |
| value_buffer.clear() | |
| if __name__ == "__main__": | |
| main(EasyDict(spaceinvaders_ppg_config)) | |