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