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| import pytest | |
| import numpy as np | |
| import pprint | |
| from easydict import EasyDict | |
| try: | |
| from dizoo.gfootball.envs.gfootball_academy_env import GfootballAcademyEnv | |
| except ModuleNotFoundError: | |
| print("[WARNING] no gfootball env, if you want to use gfootball, please install it, otherwise, ignore it.") | |
| cfg_keeper = EasyDict(dict( | |
| env_name='academy_3_vs_1_with_keeper', | |
| agent_num=3, | |
| obs_dim=26, | |
| )) | |
| cfg_counter = EasyDict(dict( | |
| env_name='academy_counterattack_hard', | |
| agent_num=4, | |
| obs_dim=34, | |
| )) | |
| class TestGfootballAcademyEnv: | |
| def get_random_action(self, min_value, max_value): | |
| action = np.random.randint(min_value, max_value + 1, (1, )) | |
| return action | |
| def test_academy_3_vs_1_with_keeper(self): | |
| cfg = cfg_keeper | |
| env = GfootballAcademyEnv(cfg) | |
| print(env.observation_space, env._action_space, env.reward_space) | |
| pp = pprint.PrettyPrinter(indent=2) | |
| for i in range(2): | |
| eps_len = 0 | |
| # env.enable_save_replay(replay_path='./video') | |
| reset_obs = env.reset() | |
| while True: | |
| eps_len += 1 | |
| action = env.random_action()[0] | |
| action = [int(action_agent) for k, action_agent in action.items()] | |
| timestep = env.step(action) | |
| obs = timestep.obs | |
| reward = timestep.reward | |
| done = timestep.done | |
| # print('observation: ') | |
| # pp.pprint(obs) | |
| assert obs['agent_state'].shape == (cfg.agent_num, cfg.obs_dim) | |
| assert obs['global_state'].shape == (cfg.agent_num, cfg.obs_dim * 2) | |
| assert obs['action_mask'].shape == (cfg.agent_num, 19) | |
| print('step {}, action: {}, reward: {}'.format(eps_len, action, reward)) | |
| if done: | |
| break | |
| assert reward == -1 or reward == 100 | |
| print(f'Episode {i} done! The episode length is {eps_len}. The last reward is {reward}.') | |
| print('End') | |
| def test_academy_counterattack_hard(self): | |
| cfg = cfg_counter | |
| env = GfootballAcademyEnv(cfg) | |
| print(env.observation_space, env._action_space, env.reward_space) | |
| pp = pprint.PrettyPrinter(indent=2) | |
| for i in range(2): | |
| eps_len = 0 | |
| reset_obs = env.reset() | |
| while True: | |
| eps_len += 1 | |
| action = env.random_action()[0] | |
| action = [int(action_agent) for k, action_agent in action.items()] | |
| timestep = env.step(action) | |
| obs = timestep.obs | |
| reward = timestep.reward | |
| done = timestep.done | |
| # print('observation: ') | |
| # pp.pprint(obs) | |
| assert obs['agent_state'].shape == (cfg.agent_num, cfg.obs_dim) | |
| assert obs['global_state'].shape == (cfg.agent_num, cfg.obs_dim * 2) | |
| assert obs['action_mask'].shape == (cfg.agent_num, 19) | |
| print('step {}, action: {}, reward: {}'.format(eps_len, action, reward)) | |
| if done: | |
| break | |
| assert reward == -1 or reward == 100 | |
| print(f'Episode {i} done! The episode length is {eps_len}. The last reward is {reward}.') | |
| print('End') | |