| import os | |
| import gym | |
| from tensorboardX import SummaryWriter | |
| from easydict import EasyDict | |
| from copy import deepcopy | |
| from ding.config import compile_config | |
| from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer | |
| from ding.envs import BaseEnvManager, DingEnvWrapper | |
| from ding.policy import PPGOffPolicy | |
| from ding.model import PPG | |
| from ding.utils import set_pkg_seed, deep_merge_dicts | |
| from dizoo.classic_control.cartpole.config.cartpole_ppg_config import cartpole_ppg_config | |
| def wrapped_cartpole_env(): | |
| return DingEnvWrapper( | |
| gym.make('CartPole-v0'), | |
| EasyDict(env_wrapper='default'), | |
| ) | |
| def main(cfg, seed=0, max_train_iter=int(1e8), max_env_step=int(1e8)): | |
| cfg = compile_config( | |
| cfg, | |
| BaseEnvManager, | |
| PPGOffPolicy, | |
| BaseLearner, | |
| SampleSerialCollector, | |
| InteractionSerialEvaluator, { | |
| 'policy': AdvancedReplayBuffer, | |
| 'value': AdvancedReplayBuffer | |
| }, | |
| save_cfg=True | |
| ) | |
| collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num | |
| collector_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(collector_env_num)], cfg=cfg.env.manager) | |
| evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], 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 = PPGOffPolicy(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 learner.train_iter >= max_train_iter or collector.envstep >= max_env_step: | |
| break | |
| if __name__ == "__main__": | |
| main(cartpole_ppg_config) | |