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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from mlagents.trainers.tests.mock_brain import make_fake_trajectory
from mlagents_envs.base_env import ActionSpec
VEC_OBS_SIZE = 6
ACTION_SIZE = 4
def test_trajectory_to_agentbuffer():
length = 15
wanted_keys = [
"next_obs_0",
"next_obs_1",
"obs_0",
"obs_1",
"memory",
"masks",
"done",
"continuous_action",
"discrete_action",
"continuous_log_probs",
"discrete_log_probs",
"action_mask",
"prev_action",
"environment_rewards",
]
wanted_keys = set(wanted_keys)
trajectory = make_fake_trajectory(
length=length,
observation_shapes=[(VEC_OBS_SIZE,), (84, 84, 3)],
action_spec=ActionSpec.create_continuous(ACTION_SIZE),
)
agentbuffer = trajectory.to_agentbuffer()
seen_keys = set()
for key, field in agentbuffer.items():
assert len(field) == length
seen_keys.add(key)
assert seen_keys == wanted_keys