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
108 行
3.6 KiB
108 行
3.6 KiB
import argparse
|
|
|
|
from mlagents_envs.environment import UnityEnvironment
|
|
from mlagents_envs.side_channel.engine_configuration_channel import (
|
|
EngineConfigurationChannel,
|
|
)
|
|
|
|
|
|
def test_run_environment(env_name):
|
|
"""
|
|
Run the low-level API test using the specified environment
|
|
:param env_name: Name of the Unity environment binary to launch
|
|
"""
|
|
engine_configuration_channel = EngineConfigurationChannel()
|
|
env = UnityEnvironment(
|
|
file_name=env_name,
|
|
side_channels=[engine_configuration_channel],
|
|
no_graphics=True,
|
|
additional_args=["-logFile", "-"],
|
|
)
|
|
|
|
try:
|
|
# Reset the environment
|
|
env.reset()
|
|
|
|
# Set the default brain to work with
|
|
group_name = list(env.behavior_specs.keys())[0]
|
|
group_spec = env.behavior_specs[group_name]
|
|
|
|
# Set the time scale of the engine
|
|
engine_configuration_channel.set_configuration_parameters(time_scale=3.0)
|
|
|
|
# Get the state of the agents
|
|
decision_steps, terminal_steps = env.get_steps(group_name)
|
|
|
|
# Examine the number of observations per Agent
|
|
print("Number of observations : ", len(group_spec.observation_shapes))
|
|
|
|
# Is there a visual observation ?
|
|
vis_obs = any(len(shape) == 3 for shape in group_spec.observation_shapes)
|
|
print("Is there a visual observation ?", vis_obs)
|
|
|
|
# Examine the state space for the first observation for the first agent
|
|
print(
|
|
"First Agent observation looks like: \n{}".format(decision_steps.obs[0][0])
|
|
)
|
|
|
|
for _episode in range(10):
|
|
env.reset()
|
|
decision_steps, terminal_steps = env.get_steps(group_name)
|
|
done = False
|
|
episode_rewards = 0
|
|
tracked_agent = -1
|
|
while not done:
|
|
action_tuple = group_spec.action_spec.random_action(len(decision_steps))
|
|
if tracked_agent == -1 and len(decision_steps) >= 1:
|
|
tracked_agent = decision_steps.agent_id[0]
|
|
env.set_actions(group_name, action_tuple)
|
|
env.step()
|
|
decision_steps, terminal_steps = env.get_steps(group_name)
|
|
done = False
|
|
if tracked_agent in decision_steps:
|
|
episode_rewards += decision_steps[tracked_agent].reward
|
|
if tracked_agent in terminal_steps:
|
|
episode_rewards += terminal_steps[tracked_agent].reward
|
|
done = True
|
|
print(f"Total reward this episode: {episode_rewards}")
|
|
finally:
|
|
env.close()
|
|
|
|
|
|
def test_closing(env_name):
|
|
"""
|
|
Run the low-level API and close the environment
|
|
:param env_name: Name of the Unity environment binary to launch
|
|
"""
|
|
try:
|
|
env1 = UnityEnvironment(
|
|
file_name=env_name,
|
|
base_port=5006,
|
|
no_graphics=True,
|
|
additional_args=["-logFile", "-"],
|
|
)
|
|
env1.close()
|
|
env1 = UnityEnvironment(
|
|
file_name=env_name,
|
|
base_port=5006,
|
|
no_graphics=True,
|
|
additional_args=["-logFile", "-"],
|
|
)
|
|
env2 = UnityEnvironment(
|
|
file_name=env_name,
|
|
base_port=5007,
|
|
no_graphics=True,
|
|
additional_args=["-logFile", "-"],
|
|
)
|
|
env2.reset()
|
|
finally:
|
|
env1.close()
|
|
env2.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--env", default="artifacts/testPlayer")
|
|
args = parser.parse_args()
|
|
test_run_environment(args.env)
|
|
test_closing(args.env)
|