Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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67 行
1.9 KiB

import argparse
from mlagents_envs.environment import UnityEnvironment
from gym_unity.envs import UnityToGymWrapper
def test_run_environment(env_name):
"""
Run the gym test using the specified environment
:param env_name: Name of the Unity environment binary to launch
"""
u_env = UnityEnvironment(env_name, worker_id=1, no_graphics=True)
env = UnityToGymWrapper(u_env)
try:
# Examine environment parameters
print(str(env))
# Reset the environment
initial_observations = env.reset()
if len(env.observation_space.shape) == 1:
# Examine the initial vector observation
print("Agent observations look like: \n{}".format(initial_observations))
for _episode in range(10):
env.reset()
done = False
episode_rewards = 0
while not done:
actions = env.action_space.sample()
obs, reward, done, _ = env.step(actions)
episode_rewards += reward
print("Total reward this episode: {}".format(episode_rewards))
finally:
env.close()
def test_closing(env_name):
"""
Run the gym test and closes the environment multiple times
:param env_name: Name of the Unity environment binary to launch
"""
try:
env1 = UnityToGymWrapper(
UnityEnvironment(env_name, worker_id=1, no_graphics=True)
)
env1.close()
env1 = UnityToGymWrapper(
UnityEnvironment(env_name, worker_id=1, no_graphics=True)
)
env2 = UnityToGymWrapper(
UnityEnvironment(env_name, worker_id=2, no_graphics=True)
)
env2.reset()
finally:
env1.close()
env2.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="Project/testPlayer")
args = parser.parse_args()
test_run_environment(args.env)
test_closing(args.env)