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67 行
2.0 KiB
67 行
2.0 KiB
import argparse
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from mlagents_envs.environment import UnityEnvironment
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from gym_unity.envs import UnityToGymWrapper
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def test_run_environment(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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u_env = UnityEnvironment(env_name, worker_id=1, no_graphics=True)
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env = UnityToGymWrapper(u_env, use_visual=False)
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try:
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# Examine environment parameters
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print(str(env))
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# Reset the environment
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initial_observations = env.reset()
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if len(env.observation_space.shape) == 1:
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# Examine the initial vector observation
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print(f"Agent observations look like: \n{initial_observations}")
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for _episode in range(10):
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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 = env.action_space.sample()
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obs, reward, done, _ = env.step(actions)
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episode_rewards += reward
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print(f"Total reward this episode: {episode_rewards}")
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finally:
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env.close()
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def test_closing(env_name):
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"""
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Run the gym test and closes the environment multiple times
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:param env_name: Name of the Unity environment binary to launch
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"""
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try:
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env1 = UnityToGymWrapper(
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UnityEnvironment(env_name, worker_id=1, no_graphics=True), use_visual=False
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)
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env1.close()
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env1 = UnityToGymWrapper(
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UnityEnvironment(env_name, worker_id=1, no_graphics=True), use_visual=False
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)
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env2 = UnityToGymWrapper(
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UnityEnvironment(env_name, worker_id=2, no_graphics=True), use_visual=False
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)
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env2.reset()
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finally:
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env1.close()
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env2.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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test_run_environment(args.env)
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test_closing(args.env)
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