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Sleeping
| import os | |
| import torch | |
| import gym | |
| import numpy as np | |
| from tensorboardX import SummaryWriter | |
| from rocket_recycling.rocket import Rocket | |
| from ditk import logging | |
| from ding.model import VAC | |
| from ding.policy import PPOPolicy | |
| from ding.envs import DingEnvWrapper, BaseEnvManagerV2, EvalEpisodeReturnWrapper | |
| from ding.config import compile_config | |
| from ding.framework import task | |
| from ding.framework.context import OnlineRLContext | |
| from ding.framework.middleware import multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \ | |
| gae_estimator, termination_checker | |
| from ding.utils import set_pkg_seed | |
| from dizoo.rocket.config.rocket_landing_ppo_config import main_config, create_config | |
| class RocketLandingWrapper(gym.Wrapper): | |
| def __init__(self, env): | |
| super().__init__(env) | |
| self._observation_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(8, ), dtype=np.float32) | |
| self._action_space = gym.spaces.Discrete(9) | |
| self._action_space.seed(0) # default seed | |
| self.reward_range = (float('-inf'), float('inf')) | |
| def wrapped_rocket_env(task, max_steps): | |
| return DingEnvWrapper( | |
| Rocket(task=task, max_steps=max_steps), | |
| cfg={'env_wrapper': [ | |
| lambda env: RocketLandingWrapper(env), | |
| lambda env: EvalEpisodeReturnWrapper(env), | |
| ]} | |
| ) | |
| def main(): | |
| logging.getLogger().setLevel(logging.INFO) | |
| main_config.exp_name = 'rocket_landing_ppo_nseed' | |
| main_config.policy.cuda = True | |
| print('torch.cuda.is_available(): ', torch.cuda.is_available()) | |
| cfg = compile_config(main_config, create_cfg=create_config, auto=True) | |
| num_seed = 4 | |
| for seed_i in range(num_seed): | |
| tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'seed' + str(seed_i))) | |
| with task.start(async_mode=False, ctx=OnlineRLContext()): | |
| collector_env = BaseEnvManagerV2( | |
| env_fn=[ | |
| lambda: wrapped_rocket_env(cfg.env.task, cfg.env.max_steps) | |
| for _ in range(cfg.env.collector_env_num) | |
| ], | |
| cfg=cfg.env.manager | |
| ) | |
| evaluator_env = BaseEnvManagerV2( | |
| env_fn=[ | |
| lambda: wrapped_rocket_env(cfg.env.task, cfg.env.max_steps) | |
| for _ in range(cfg.env.evaluator_env_num) | |
| ], | |
| cfg=cfg.env.manager | |
| ) | |
| # evaluator_env.enable_save_replay() | |
| set_pkg_seed(seed_i, use_cuda=cfg.policy.cuda) | |
| model = VAC(**cfg.policy.model) | |
| policy = PPOPolicy(cfg.policy, model=model) | |
| def _add_scalar(ctx): | |
| if ctx.eval_value != -np.inf: | |
| tb_logger.add_scalar('evaluator_step/reward', ctx.eval_value, global_step=ctx.env_step) | |
| collector_rewards = [ctx.trajectories[i]['reward'] for i in range(len(ctx.trajectories))] | |
| collector_mean_reward = sum(collector_rewards) / len(ctx.trajectories) | |
| collector_max_reward = max(collector_rewards) | |
| collector_min_reward = min(collector_rewards) | |
| tb_logger.add_scalar('collecter_step/mean_reward', collector_mean_reward, global_step=ctx.env_step) | |
| tb_logger.add_scalar('collecter_step/max_reward', collector_max_reward, global_step=ctx.env_step) | |
| tb_logger.add_scalar('collecter_step/min_reward', collector_min_reward, global_step=ctx.env_step) | |
| task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) | |
| task.use(StepCollector(cfg, policy.collect_mode, collector_env)) | |
| task.use(gae_estimator(cfg, policy.collect_mode)) | |
| task.use(multistep_trainer(cfg, policy.learn_mode)) | |
| task.use(CkptSaver(policy, cfg.exp_name, train_freq=100)) | |
| # task.use(_add_scalar) | |
| task.use(termination_checker(max_env_step=int(3e6))) | |
| task.run() | |
| if __name__ == "__main__": | |
| main() | |