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