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| import random | |
| import numpy as np | |
| from dizoo.gfootball.envs.obs.gfootball_obs import PlayerObs, MatchObs | |
| from ding.utils.data import default_collate | |
| def generate_data(player_obs: dict) -> np.array: | |
| dim = player_obs['dim'] | |
| min = player_obs['value']['min'] | |
| max = player_obs['value']['max'] | |
| dinfo = player_obs['value']['dinfo'] | |
| if dinfo in ['one-hot', 'boolean vector']: | |
| data = np.zeros((dim, ), dtype=np.float32) | |
| data[random.randint(0, dim - 1)] = 1 | |
| return data | |
| elif dinfo == 'float': | |
| data = np.random.rand(dim) | |
| for dim_idx in range(dim): | |
| data[dim_idx] = min[dim_idx] + (max[dim_idx] - min[dim_idx]) * data[dim_idx] | |
| return data | |
| class FakeGfootballDataset: | |
| def __init__(self): | |
| match_obs = MatchObs({}) | |
| player_obs = PlayerObs({}) | |
| self.match_obs_info = match_obs.template | |
| self.player_obs_info = player_obs.template | |
| self.action_dim = 19 | |
| self.batch_size = 4 | |
| del match_obs, player_obs | |
| def __len__(self) -> int: | |
| return self.batch_size | |
| def get_random_action(self) -> np.array: | |
| return np.random.randint(0, self.action_dim - 1, size=(1, )) | |
| def get_random_obs(self) -> dict: | |
| inputs = {} | |
| for match_obs in self.match_obs_info: | |
| key = match_obs['ret_key'] | |
| data = generate_data(match_obs) | |
| inputs[key] = data | |
| players_list = [] | |
| for _ in range(22): | |
| one_player = {} | |
| for player_obs in self.player_obs_info: | |
| key = player_obs['ret_key'] | |
| data = generate_data(player_obs) | |
| one_player[key] = data | |
| players_list.append(one_player) | |
| inputs['players'] = players_list | |
| return inputs | |
| def get_batched_obs(self, bs: int) -> dict: | |
| batch = [] | |
| for _ in range(bs): | |
| batch.append(self.get_random_obs()) | |
| return default_collate(batch) | |
| def get_random_reward(self) -> np.array: | |
| return np.array([random.random() - 0.5]) | |
| def get_random_terminals(self) -> int: | |
| sample = random.random() | |
| if sample > 0.99: | |
| return 1 | |
| return 0 | |
| def get_batch_sample(self, bs: int) -> list: | |
| batch = [] | |
| for _ in range(bs): | |
| step = {} | |
| step['obs'] = self.get_random_obs() | |
| step['next_obs'] = self.get_random_obs() | |
| step['action'] = self.get_random_action() | |
| step['done'] = self.get_random_terminals() | |
| step['reward'] = self.get_random_reward() | |
| batch.append(step) | |
| return batch | |