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48 行
1.4 KiB
48 行
1.4 KiB
import argparse
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import numpy as np
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from gym_unity.envs import UnityEnv
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def main(env_name):
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"""
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Run the gym test using the specified environment
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:param env_name: Name of the Unity environment binary to launch
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"""
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multi_env = UnityEnv(
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env_name, worker_id=1, use_visual=False, multiagent=True, no_graphics=True
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)
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try:
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# Examine environment parameters
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print(str(multi_env))
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# Reset the environment
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initial_observations = multi_env.reset()
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if len(multi_env.observation_space.shape) == 1:
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# Examine the initial vector observation
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print("Agent observations look like: \n{}".format(initial_observations[0]))
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for _episode in range(10):
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multi_env.reset()
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done = False
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episode_rewards = 0
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while not done:
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actions = [
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multi_env.action_space.sample()
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for agent in range(multi_env.number_agents)
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]
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observations, rewards, dones, info = multi_env.step(actions)
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episode_rewards += np.mean(rewards)
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done = dones[0]
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print("Total reward this episode: {}".format(episode_rewards))
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finally:
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multi_env.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--env", default="Project/testPlayer")
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args = parser.parse_args()
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main(args.env)
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