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import os |
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from easydict import EasyDict |
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from tensorboardX import SummaryWriter |
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from ding.config import compile_config |
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from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer |
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from ding.envs.env_manager.envpool_env_manager import PoolEnvManager |
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from ding.policy import DQNPolicy |
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from ding.model import DQN |
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from ding.utils import set_pkg_seed |
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from ding.rl_utils import get_epsilon_greedy_fn |
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from dizoo.atari.config.serial import pong_dqn_envpool_config |
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def main(cfg, seed=0, max_iterations=int(1e10)): |
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cfg.exp_name = 'atari_dqn_envpool' |
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cfg = compile_config( |
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cfg, |
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PoolEnvManager, |
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DQNPolicy, |
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BaseLearner, |
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SampleSerialCollector, |
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InteractionSerialEvaluator, |
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AdvancedReplayBuffer, |
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save_cfg=True |
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) |
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collector_env_cfg = EasyDict( |
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{ |
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'env_id': cfg.env.env_id, |
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'env_num': cfg.env.collector_env_num, |
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'batch_size': cfg.env.collector_batch_size, |
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'episodic_life': True, |
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'reward_clip': True, |
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'gray_scale': cfg.env.get('gray_scale', True), |
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'stack_num': cfg.env.get('stack_num', 4), |
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'frame_skip': cfg.env.get('frame_skip', 4), |
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} |
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) |
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collector_env = PoolEnvManager(collector_env_cfg) |
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evaluator_env_cfg = EasyDict( |
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{ |
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'env_id': cfg.env.env_id, |
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'env_num': cfg.env.evaluator_env_num, |
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'batch_size': cfg.env.evaluator_batch_size, |
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'episodic_life': False, |
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'reward_clip': False, |
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'gray_scale': cfg.env.get('gray_scale', True), |
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'stack_num': cfg.env.get('stack_num', 4), |
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'frame_skip': cfg.env.get('frame_skip', 4), |
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} |
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) |
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evaluator_env = PoolEnvManager(evaluator_env_cfg) |
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collector_env.seed(seed) |
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evaluator_env.seed(seed) |
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set_pkg_seed(seed, use_cuda=cfg.policy.cuda) |
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model = DQN(**cfg.policy.model) |
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policy = DQNPolicy(cfg.policy, model=model) |
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tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) |
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learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) |
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collector = SampleSerialCollector( |
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cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name |
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) |
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evaluator = InteractionSerialEvaluator( |
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cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name |
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) |
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replay_buffer = AdvancedReplayBuffer( |
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cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name, instance_name='replay_buffer' |
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) |
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eps_cfg = cfg.policy.other.eps |
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epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type) |
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while True: |
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if evaluator.should_eval(learner.train_iter): |
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stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) |
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if stop: |
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break |
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eps = epsilon_greedy(collector.envstep) |
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new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps}) |
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replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) |
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for i in range(cfg.policy.learn.update_per_collect): |
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batch_size = learner.policy.get_attribute('batch_size') |
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train_data = replay_buffer.sample(batch_size, learner.train_iter) |
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if train_data is not None: |
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learner.train(train_data, collector.envstep) |
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if __name__ == "__main__": |
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main(EasyDict(pong_dqn_envpool_config)) |
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