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import os |
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import gym |
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import gym_hybrid |
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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 import BaseEnvManager |
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from ding.policy import DDPGPolicy |
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from ding.model import ContinuousQAC |
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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.gym_hybrid.envs.gym_hybrid_env import GymHybridEnv |
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from dizoo.gym_hybrid.config.gym_hybrid_ddpg_config import gym_hybrid_ddpg_config |
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def main(cfg, seed=0): |
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cfg = compile_config( |
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cfg, |
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BaseEnvManager, |
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DDPGPolicy, |
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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_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num |
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collector_env = BaseEnvManager( |
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env_fn=[lambda: GymHybridEnv(cfg=cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager |
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) |
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evaluator_env = BaseEnvManager( |
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env_fn=[lambda: GymHybridEnv(cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager |
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) |
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collector_env.seed(seed) |
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evaluator_env.seed(seed, dynamic_seed=False) |
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set_pkg_seed(seed, use_cuda=cfg.policy.cuda) |
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model = ContinuousQAC(**cfg.policy.model) |
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policy = DDPGPolicy(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(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) |
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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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train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) |
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if train_data is None: |
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break |
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learner.train(train_data, collector.envstep) |
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evaluator_env = BaseEnvManager( |
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env_fn=[lambda: GymHybridEnv(cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager |
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) |
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evaluator_env.enable_save_replay(cfg.env.replay_path) |
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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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evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) |
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if __name__ == "__main__": |
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main(gym_hybrid_ddpg_config, seed=0) |
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