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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")
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