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| """ | |
| Overview: | |
| The following is to show some statistics of the dataset in gfootball env. | |
| """ | |
| import torch | |
| import numpy as np | |
| import os | |
| from ding.config import read_config, compile_config | |
| from ding.utils.data import create_dataset | |
| from dizoo.gfootball.entry.gfootball_bc_config import main_config, create_config | |
| path = os.path.abspath(__file__) | |
| dir_path = os.path.dirname(path) | |
| if __name__ == "__main__": | |
| config = [main_config, create_config] | |
| input_cfg = config | |
| if isinstance(input_cfg, str): | |
| cfg, create_cfg = read_config(input_cfg) | |
| else: | |
| cfg, create_cfg = input_cfg | |
| cfg = compile_config(cfg, seed=0, auto=True, create_cfg=create_cfg) | |
| cfg.policy.collect.data_type = 'naive' | |
| """episode data""" | |
| # Users should add their own BC data path here. | |
| cfg.policy.collect.data_path = dir_path + '/gfootball_rule_100eps.pkl' | |
| dataset = create_dataset(cfg) | |
| print('num_episodes', dataset.__len__()) | |
| print('episode 0, transition 0', dataset.__getitem__(0)[0]) | |
| episodes_len = np.array([len(dataset.__getitem__(i)) for i in range(dataset.__len__())]) | |
| print('episodes_len', episodes_len) | |
| return_of_episode = torch.stack( | |
| [ | |
| torch.stack( | |
| [dataset.__getitem__(episode)[i]['reward'] for i in range(dataset.__getitem__(episode).__len__())], | |
| axis=0 | |
| ).sum(0) for episode in range(dataset.__len__()) | |
| ], | |
| axis=0 | |
| ) | |
| print('return_of_episode', return_of_episode) | |
| print(return_of_episode.mean(), return_of_episode.max(), return_of_episode.min()) | |
| """transition data""" | |
| # Users should add their own BC data path here. | |
| cfg.policy.collect.data_path = dir_path + '/gfootball_rule_100eps_transitions_lt0.pkl' | |
| dataset = create_dataset(cfg) | |
| print('num_transitions', dataset.__len__()) | |
| print('transition 0: ', dataset.__getitem__(0)) | |
| reward_of_transitions = torch.stack( | |
| [dataset.__getitem__(transition)['reward'] for transition in range(dataset.__len__())], axis=0 | |
| ) | |
| print('reward_of_transitions', reward_of_transitions) | |
| print(reward_of_transitions.mean(), reward_of_transitions.max(), reward_of_transitions.min()) | |