58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
import warnings
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import gymnasium as gym
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from tianshou.env import ShmemVectorEnv, VectorEnvNormObs
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from tianshou.highlevel.env import ContinuousEnvironments, EnvFactory
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from tianshou.highlevel.config import RLSamplingConfig
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try:
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import envpool
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except ImportError:
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envpool = None
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def make_mujoco_env(task: str, seed: int, num_train_envs: int, num_test_envs: int, obs_norm: bool):
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"""Wrapper function for Mujoco env.
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If EnvPool is installed, it will automatically switch to EnvPool's Mujoco env.
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:return: a tuple of (single env, training envs, test envs).
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"""
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if envpool is not None:
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train_envs = env = envpool.make_gymnasium(task, num_envs=num_train_envs, seed=seed)
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test_envs = envpool.make_gymnasium(task, num_envs=num_test_envs, seed=seed)
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else:
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warnings.warn(
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"Recommend using envpool (pip install envpool) "
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"to run Mujoco environments more efficiently.",
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)
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env = gym.make(task)
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train_envs = ShmemVectorEnv([lambda: gym.make(task) for _ in range(num_train_envs)])
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test_envs = ShmemVectorEnv([lambda: gym.make(task) for _ in range(num_test_envs)])
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train_envs.seed(seed)
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test_envs.seed(seed)
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if obs_norm:
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# obs norm wrapper
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train_envs = VectorEnvNormObs(train_envs)
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test_envs = VectorEnvNormObs(test_envs, update_obs_rms=False)
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test_envs.set_obs_rms(train_envs.get_obs_rms())
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return env, train_envs, test_envs
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class MujocoEnvFactory(EnvFactory):
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def __init__(self, task: str, seed: int, sampling_config: RLSamplingConfig):
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self.task = task
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self.sampling_config = sampling_config
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self.seed = seed
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def create_envs(self) -> ContinuousEnvironments:
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env, train_envs, test_envs = make_mujoco_env(
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task=self.task,
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seed=self.seed,
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num_train_envs=self.sampling_config.num_train_envs,
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num_test_envs=self.sampling_config.num_test_envs,
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obs_norm=True,
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)
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return ContinuousEnvironments(env=env, train_envs=train_envs, test_envs=test_envs)
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