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