MLPY/Lib/site-packages/pettingzoo/test/bombardment_test.py

import random
from copy import copy

import numpy as np

from .api_test import test_observation


def bombardment_test(env, cycles=10000):
    print("Starting bombardment test")

    env.reset()
    prev_observe, _, _, _ = env.last()
    observation_0 = copy(prev_observe)
    for i in range(cycles):
        if i == cycles / 2:
            print("\t50% through bombardment test")
        for agent in env.agent_iter(env.num_agents):  # step through every agent once with observe=True
            obs, reward, done, info = env.last()
            if done:
                action = None
            elif isinstance(obs, dict) and 'action_mask' in obs:
                action = random.choice(np.flatnonzero(obs['action_mask']))
            else:
                action = env.action_space(agent).sample()
            next_observe = env.step(action)
            assert env.observation_space(agent).contains(prev_observe), "Agent's observation is outside of its observation space"
            test_observation(prev_observe, observation_0)
            prev_observe = next_observe
        env.reset()
        prev_observe, _, _, _ = env.last()
    print("Passed bombardment test")