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name: envs-and-vectorization description: "Integrate Gymnasium and PettingZoo environments with Tianshou vector envs, workers, wrappers, space introspection, seeding, and optional environment backends." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tianshou Environments and Vectorization

Use this sub-skill when work mentions DummyVectorEnv, SubprocVectorEnv, ShmemVectorEnv, RayVectorEnv, PettingZooEnv, action masks, custom Gymnasium environments, vectorized rollouts, environment workers, wrappers, seeding, or optional engines such as EnvPool, MuJoCo, Atari, VizDoom, Box2D, or robotics.

Route First

• For algorithm, policy, replay buffer, collector, or trainer construction, use ../procedural-training/SKILL.md after the environment interface is valid.
• For high-level experiment EnvFactoryRegistered setup, use ../highlevel-experiments/SKILL.md; this sub-skill only covers lower-level env factories and vector env behavior.
• For multi-agent algorithm choices beyond PettingZoo wrapping and masks, use ../offline-and-specialized-rl/SKILL.md after confirming the environment observation/action contract.

Fast Path

1. Build each environment through a zero-argument factory such as lambda: gym.make("CartPole-v1"); never pass already-created env instances into Tianshou vector env constructors.
2. Start with DummyVectorEnv(env_fns) for API validation, deterministic debugging, custom wrappers, and smoke checks.
3. Move to SubprocVectorEnv(env_fns, context="spawn" or "fork") or ShmemVectorEnv(env_fns) only after the factories are picklable/importable and observations are compatible with batching.
4. Use RayVectorEnv(env_fns) only when ray is installed and distributed execution is actually needed.
5. Call reset() before stepping, pass one action per selected env id, reset done env ids yourself after terminated | truncated, and always call close().
6. For PettingZoo AEC envs, wrap with PettingZooEnv; expect observations shaped as dictionaries containing obs, agent_id, and usually mask for discrete legal actions.

References

• references/api-reference.md lists constructors, vector env methods, worker behavior, wrappers, PettingZooEnv, and SpaceInfo.
• references/workflows.md gives custom env, vectorization, seeding, PettingZoo/action-mask, and optional-backend workflows.
• references/troubleshooting.md maps common install/import, optional dependency, validation, CLI/API misuse, and workflow failures to fixes.
• scripts/check_vector_env.py is a tiny CartPole smoke check that validates Tianshou vector env calls without training.

Safe Smoke Check

Run the bundled helper from the generated skill tree after installing public Tianshou dependencies:

python skills/tianshou/sub-skills/envs-and-vectorization/scripts/check_vector_env.py --num-envs 2 --steps 4

Add --subproc only after the default dummy run passes; subprocess mode is intentionally conservative and uses a top-level factory to avoid common pickling pitfalls.