您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
67 行
2.0 KiB
67 行
2.0 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, use_visual=False)
|
|
|
|
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), use_visual=False
|
|
)
|
|
env1.close()
|
|
env1 = UnityToGymWrapper(
|
|
UnityEnvironment(env_name, worker_id=1, no_graphics=True), use_visual=False
|
|
)
|
|
env2 = UnityToGymWrapper(
|
|
UnityEnvironment(env_name, worker_id=2, no_graphics=True), use_visual=False
|
|
)
|
|
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)
|