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| import numpy as np | |
| def max_cycles_test(mod): | |
| max_cycles = 4 | |
| parallel_env = mod.parallel_env(max_cycles=max_cycles) | |
| observations = parallel_env.reset() | |
| dones = {agent: False for agent in parallel_env.agents} | |
| test_cycles = max_cycles + 10 # allows environment to do more than max_cycles if it so wishes | |
| for step in range(test_cycles): | |
| actions = {agent: parallel_env.action_space(agent).sample() for agent in parallel_env.agents if not dones[agent]} | |
| observations, rewards, dones, infos = parallel_env.step(actions) | |
| if all(dones.values()): | |
| break | |
| pstep = step + 1 | |
| env = mod.env(max_cycles=max_cycles) | |
| env.reset() | |
| agent_counts = np.zeros(len(env.possible_agents)) | |
| for a in env.agent_iter(): | |
| # counts agent index | |
| aidx = env.possible_agents.index(a) | |
| agent_counts[aidx] += 1 | |
| action = env.action_space(a).sample() if not env.dones[a] else None | |
| env.step(action) | |
| assert max_cycles == pstep | |
| # does not check the minimum value because some agents might be killed before | |
| # all the steps are complete. However, most agents should still be alive | |
| # given a short number of cycles | |
| assert max_cycles == np.max(agent_counts) - 1 | |
| assert max_cycles == np.median(agent_counts) - 1 | |