Pendulum-v1-TD3 / policy_config.py
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exp_config = {
'env': {
'manager': {
'episode_num': float("inf"),
'max_retry': 1,
'retry_type': 'reset',
'auto_reset': True,
'step_timeout': None,
'reset_timeout': None,
'retry_waiting_time': 0.1,
'cfg_type': 'BaseEnvManagerDict'
},
'stop_value': -250,
'n_evaluator_episode': 5,
'env_id': 'Pendulum-v1',
'collector_env_num': 8,
'evaluator_env_num': 5,
'act_scale': True
},
'policy': {
'model': {
'twin_critic': True,
'obs_shape': 3,
'action_shape': 1,
'action_space': 'regression'
},
'learn': {
'learner': {
'train_iterations': 1000000000,
'dataloader': {
'num_workers': 0
},
'log_policy': True,
'hook': {
'load_ckpt_before_run': '',
'log_show_after_iter': 100,
'save_ckpt_after_iter': 10000,
'save_ckpt_after_run': True
},
'cfg_type': 'BaseLearnerDict'
},
'update_per_collect': 2,
'batch_size': 128,
'learning_rate_actor': 0.001,
'learning_rate_critic': 0.001,
'ignore_done': True,
'target_theta': 0.005,
'discount_factor': 0.99,
'actor_update_freq': 2,
'noise': True,
'noise_sigma': 0.1,
'noise_range': {
'min': -0.5,
'max': 0.5
}
},
'collect': {
'collector': {
'collect_print_freq': 1000
},
'unroll_len': 1,
'noise_sigma': 0.1,
'n_sample': 48
},
'eval': {
'evaluator': {
'eval_freq': 100,
'render': {
'render_freq': -1,
'mode': 'train_iter'
},
'figure_path': None,
'cfg_type': 'InteractionSerialEvaluatorDict',
'stop_value': -250,
'n_episode': 5
}
},
'other': {
'replay_buffer': {
'replay_buffer_size': 20000
}
},
'on_policy': False,
'cuda': False,
'multi_gpu': False,
'bp_update_sync': True,
'traj_len_inf': False,
'type': 'td3',
'priority': False,
'priority_IS_weight': False,
'random_collect_size': 800,
'transition_with_policy_data': False,
'action_space': 'continuous',
'reward_batch_norm': False,
'multi_agent': False,
'cfg_type': 'TD3PolicyDict'
},
'exp_name': 'Pendulum-v1-TD3',
'seed': 0,
'wandb_logger': {
'gradient_logger': True,
'video_logger': True,
'plot_logger': True,
'action_logger': True,
'return_logger': False
}
}