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import os |
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import gym |
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import torch |
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from tensorboardX import SummaryWriter |
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from easydict import EasyDict |
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from functools import partial |
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from ding.config import compile_config |
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from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer |
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from ding.envs import BaseEnvManager |
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from ding.envs import get_vec_env_setting, create_env_manager |
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from ding.policy import DDPGPolicy |
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from ding.utils import set_pkg_seed |
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cartpole_balance_ddpg_config = dict( |
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exp_name='dmc2gym_cartpole_balance_ddpg_eval', |
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env=dict( |
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env_id='dmc2gym_cartpole_balance', |
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domain_name='cartpole', |
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task_name='balance', |
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from_pixels=False, |
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norm_obs=dict(use_norm=False, ), |
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norm_reward=dict(use_norm=False, ), |
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collector_env_num=1, |
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evaluator_env_num=8, |
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use_act_scale=True, |
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n_evaluator_episode=8, |
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replay_path='./dmc2gym_cartpole_balance_ddpg_eval/video', |
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stop_value=1000, |
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), |
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policy=dict( |
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cuda=True, |
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random_collect_size=2560, |
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load_path="./dmc2gym_cartpole_balance_ddpg/ckpt/iteration_10000.pth.tar", |
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model=dict( |
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obs_shape=5, |
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action_shape=1, |
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twin_critic=False, |
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actor_head_hidden_size=128, |
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critic_head_hidden_size=128, |
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action_space='regression', |
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), |
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learn=dict( |
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update_per_collect=1, |
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batch_size=128, |
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learning_rate_actor=1e-3, |
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learning_rate_critic=1e-3, |
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ignore_done=False, |
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target_theta=0.005, |
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discount_factor=0.99, |
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actor_update_freq=1, |
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noise=False, |
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), |
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collect=dict( |
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n_sample=1, |
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unroll_len=1, |
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noise_sigma=0.1, |
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), |
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other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ), |
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) |
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) |
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cartpole_balance_ddpg_config = EasyDict(cartpole_balance_ddpg_config) |
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main_config = cartpole_balance_ddpg_config |
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cartpole_balance_create_config = dict( |
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env=dict( |
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type='dmc2gym', |
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import_names=['dizoo.dmc2gym.envs.dmc2gym_env'], |
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), |
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env_manager=dict(type='base'), |
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policy=dict( |
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type='ddpg', |
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import_names=['ding.policy.ddpg'], |
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), |
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replay_buffer=dict(type='naive', ), |
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) |
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cartpole_balance_create_config = EasyDict(cartpole_balance_create_config) |
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create_config = cartpole_balance_create_config |
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def main(cfg, create_cfg, seed=0): |
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cfg = compile_config( |
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cfg, |
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BaseEnvManager, |
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DDPGPolicy, |
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BaseLearner, |
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SampleSerialCollector, |
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InteractionSerialEvaluator, |
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AdvancedReplayBuffer, |
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create_cfg=create_cfg, |
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save_cfg=True |
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) |
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create_cfg.policy.type = create_cfg.policy.type + '_command' |
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env_fn = None |
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cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True) |
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env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env) |
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evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) |
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evaluator_env.enable_save_replay(cfg.env.replay_path) |
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evaluator_env.seed(seed, dynamic_seed=False) |
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set_pkg_seed(seed, use_cuda=cfg.policy.cuda) |
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policy = DDPGPolicy(cfg.policy) |
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policy.eval_mode.load_state_dict(torch.load(cfg.policy.load_path, map_location='cpu')) |
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tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) |
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evaluator = InteractionSerialEvaluator( |
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cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name |
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) |
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evaluator.eval() |
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if __name__ == "__main__": |
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main(main_config, create_config, seed=0) |
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