VAGEN / visual_scout /maniskill_vector_env.py
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"""
ManiSkill StackThreeCube 向量化环境构造。
- ``sim_backend=gpu``:单进程内 ``num_envs=n``(GPU 批量仿真)。
- ``sim_backend=cpu``:禁止单进程 ``num_envs>1``,使用 ``AsyncVectorEnv`` 多进程,每进程 ``num_envs=1``。
SAC / PPO 等脚本应复用 ``make_batched_stack_threecube_env``,避免再踩坑。
"""
from __future__ import annotations
import os
from typing import Any
def single_action_space(env: Any) -> Any:
"""ManiSkill 批量 env 与 gymnasium.VectorEnv:用 single_action_space 表示单环境动作。"""
return getattr(env, "single_action_space", env.action_space)
def make_batched_stack_threecube_env(
*,
env_id: str,
obs_mode: str,
control_mode: str,
sim_backend: str,
render_backend: str,
num_envs: int,
) -> Any:
import gymnasium as gym
from gymnasium.vector import AsyncVectorEnv
import mani_skill.envs # noqa: F401 — 注册 ManiSkill 内置环境
import vagen.env.primitive_skill.maniskill.env # noqa: F401 — 注册 StackThreeCube
n = int(num_envs)
sb = str(sim_backend).lower()
if sb == "cpu" and n > 1:
def make_one() -> Any:
# Worker 进程里强制不暴露 CUDA,避免 CPU-sim reset 触发 torch.cuda 初始化。
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
return gym.make(
env_id,
num_envs=1,
obs_mode=obs_mode,
control_mode=control_mode,
render_mode="rgb_array",
sim_backend=sim_backend,
render_backend=render_backend,
)
if n >= 32:
print(
f"[maniskill_vector_env] CPU 仿真 + AsyncVectorEnv:将启动 {n} 个子进程;"
"大规模训练请使用 --sim-backend gpu。"
)
return AsyncVectorEnv(
[make_one for _ in range(n)],
shared_memory=False,
context="spawn",
)
return gym.make(
env_id,
num_envs=n,
obs_mode=obs_mode,
control_mode=control_mode,
render_mode="rgb_array",
sim_backend=sim_backend,
render_backend=render_backend,
)