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from ditk import logging |
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from ding.model import ContinuousQAC |
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from ding.policy import SACPolicy |
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from ding.envs import BaseEnvManagerV2 |
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from ding.data import DequeBuffer |
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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 data_pusher, StepCollector, interaction_evaluator, \ |
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CkptSaver, OffPolicyLearner, termination_checker |
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from ding.utils import set_pkg_seed |
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from dizoo.dmc2gym.envs.dmc2gym_env import DMC2GymEnv |
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from dizoo.dmc2gym.config.dmc2gym_sac_state_config import main_config, create_config |
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import numpy as np |
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from tensorboardX import SummaryWriter |
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import os |
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def main(): |
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logging.getLogger().setLevel(logging.INFO) |
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main_config.exp_name = 'dmc2gym_sac_state_nseed_5M' |
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main_config.policy.cuda = True |
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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=[lambda: DMC2GymEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager |
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) |
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evaluator_env = BaseEnvManagerV2( |
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env_fn=[lambda: DMC2GymEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager |
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) |
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set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) |
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model = ContinuousQAC(**cfg.policy.model) |
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buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) |
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policy = SACPolicy(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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tb_logger.add_scalar('collecter_step/mean_reward', collector_mean_reward, global_step=ctx.env_step) |
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tb_logger.add_scalar( |
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'collecter_step/avg_env_step_per_episode', |
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ctx.env_step / ctx.env_episode, |
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global_step=ctx.env_step |
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) |
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def _add_train_scalar(ctx): |
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len_train = len(ctx.train_output) |
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cur_lr_q_avg = sum([ctx.train_output[i]['cur_lr_q'] for i in range(len_train)]) / len_train |
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cur_lr_p_avg = sum([ctx.train_output[i]['cur_lr_p'] for i in range(len_train)]) / len_train |
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critic_loss_avg = sum([ctx.train_output[i]['critic_loss'] for i in range(len_train)]) / len_train |
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policy_loss_avg = sum([ctx.train_output[i]['policy_loss'] for i in range(len_train)]) / len_train |
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total_loss_avg = sum([ctx.train_output[i]['total_loss'] for i in range(len_train)]) / len_train |
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tb_logger.add_scalar('learner_step/cur_lr_q_avg', cur_lr_q_avg, global_step=ctx.env_step) |
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tb_logger.add_scalar('learner_step/cur_lr_p_avg', cur_lr_p_avg, global_step=ctx.env_step) |
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tb_logger.add_scalar('learner_step/critic_loss_avg', critic_loss_avg, global_step=ctx.env_step) |
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tb_logger.add_scalar('learner_step/policy_loss_avg', policy_loss_avg, global_step=ctx.env_step) |
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tb_logger.add_scalar('learner_step/total_loss_avg', total_loss_avg, 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( |
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StepCollector( |
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cfg, policy.collect_mode, collector_env, random_collect_size=cfg.policy.random_collect_size |
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) |
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) |
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task.use(_add_scalar) |
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task.use(data_pusher(cfg, buffer_)) |
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task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_)) |
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task.use(_add_train_scalar) |
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task.use(CkptSaver(policy, cfg.exp_name, train_freq=int(1e5))) |
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task.use(termination_checker(max_env_step=int(5e6))) |
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task.run() |
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if __name__ == "__main__": |
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main() |
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