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