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import random
import time
import numpy as np
def performance_benchmark(env):
print("Starting performance benchmark")
cycles = 0
turn = 0
env.reset()
start = time.time()
end = 0
while True:
cycles += 1
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()
env.step(action)
turn += 1
if all(env.dones.values()):
env.reset()
if time.time() - start > 5:
end = time.time()
break
length = end - start
turns_per_time = turn / length
cycles_per_time = cycles / length
print(str(turns_per_time) + " turns per second")
print(str(cycles_per_time) + " cycles per second")
print("Finished performance benchmark")
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