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| import os | |
| import gym | |
| from tensorboardX import SummaryWriter | |
| from torch.optim.lr_scheduler import LambdaLR | |
| from ding.config import compile_config | |
| from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer | |
| from ding.envs import BaseEnvManager, DingEnvWrapper | |
| from ding.policy import DDPGPolicy | |
| from ding.model import ContinuousQAC | |
| from ding.utils import set_pkg_seed | |
| from dizoo.classic_control.pendulum.envs import PendulumEnv | |
| from dizoo.classic_control.pendulum.config.pendulum_td3_config import pendulum_td3_config | |
| def main(cfg, seed=0): | |
| cfg = compile_config( | |
| cfg, | |
| BaseEnvManager, | |
| DDPGPolicy, | |
| BaseLearner, | |
| SampleSerialCollector, | |
| InteractionSerialEvaluator, | |
| AdvancedReplayBuffer, | |
| save_cfg=True | |
| ) | |
| # Set up envs for collection and evaluation | |
| collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num | |
| collector_env = BaseEnvManager( | |
| env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager | |
| ) | |
| evaluator_env = BaseEnvManager( | |
| env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager | |
| ) | |
| # Set random seed for all package and instance | |
| collector_env.seed(seed) | |
| evaluator_env.seed(seed, dynamic_seed=False) | |
| set_pkg_seed(seed, use_cuda=cfg.policy.cuda) | |
| # Set up RL Policy | |
| model = ContinuousQAC(**cfg.policy.model) | |
| policy = DDPGPolicy(cfg.policy, model=model) | |
| # lr_scheduler demo | |
| lr_scheduler = LambdaLR( | |
| policy.learn_mode.get_attribute('optimizer_actor'), lr_lambda=lambda iters: min(1.0, 0.5 + 0.5 * iters / 1000) | |
| ) | |
| # Set up collection, training and evaluation utilities | |
| 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 | |
| ) | |
| replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) | |
| # Training & Evaluation loop | |
| while True: | |
| # Evaluate at the beginning and with specific frequency | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) | |
| if stop: | |
| break | |
| # Collect data from environments | |
| new_data = collector.collect(train_iter=learner.train_iter) | |
| replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) | |
| # Train | |
| for i in range(cfg.policy.learn.update_per_collect): | |
| train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) | |
| if train_data is None: | |
| break | |
| learner.train(train_data, collector.envstep) | |
| lr_scheduler.step() | |
| tb_logger.add_scalar('other_iter/scheduled_lr', lr_scheduler.get_last_lr()[0], learner.train_iter) | |
| if __name__ == "__main__": | |
| main(pendulum_td3_config, seed=0) | |