env-ssl-wrapper
One line turns any simulator's environment — gymnasium, dm_control, isaac, maniskill, pybullet, robosuite, pufferlib — into the same torch-native interface.
Install
pip install env-ssl-wrapper
Usage
import torch
from env_ssl_wrapper import compose_env
env = compose_env(
any_env, # any env from any sim
('tensor', dict(device='cpu')), # wrap with whatever you need
'done_tracker',
)
obs, info = env.reset() # torch.float32, batched
while not env.all_done:
actions = torch.randint(0, 2, (8,))
obs, reward, terminated, truncated, info = env.step(actions)
Works identically for every simulator.
Wrappers
Pass wrappers as strings (default config) or (name, dict) tuples (custom config), in any order.
| Wrapper | What it does |
|---|---|
standardize |
Normalizes any sim's step/reset signatures, vectorization, and autoreset into (obs, reward, terminated, truncated, info). Applied automatically. |
time_limit |
Caps episodes, sets truncated=True. ('time_limit', dict(max_timesteps=200)) |
done_tracker |
Tracks per-env episode_lengths, exposes env.all_done / env.needs_reset. |
pad_episodes |
Standardizes padding for uneven vectorized episodes: done envs emit zeros (float/int) / False (bool) obs, and rewards are zeroed from the step after termination onward (the terminating step's own reward is the real terminal transition reward and is preserved). Applied automatically to vectorized envs. Works for autoreset (Isaac, gymnasium) and non-autoreset (pufferlib, maniskill) envs alike. |
auto_batch |
Gives single envs a leading batch dim: (4,) → (1, 4). |
action_transform |
Rescales actions from a canonical (0, 1) range to the env's bounds. |
tensor |
NumPy → torch on a device, torch actions → numpy for the sim. |
flatten_obs |
Flattens dict/tuple observations into a single vector. |
Every env emits the same contract: obs torch.float32, rewards torch.float32, terminated/truncated torch.bool. env.seed(n) works on every sim.
Terminated envs are uniformly padded (zeros / False obs; rewards zeroed only after the terminating step, so the terminal transition's reward is never lost), and info['final_observation'] — the true terminal obs, frozen per env and re-emitted while the env stays done — is always present once any env has terminated, with info['_final_observation'] masking which envs it applies to. env.is_done always reflects the per-env done mask.
Mock sims
env_ssl_wrapper.mocks ships dependency-free stand-ins emulating each simulator's quirks (GymnasiumMockEnv, IsaacMockEnv, DMControlMockEnv, ...) for testing your code without installing the real sims.
from env_ssl_wrapper.mocks import IsaacMockEnv
env = compose_env(IsaacMockEnv(), 'tensor', 'done_tracker')
Tests
uv sync --extra test
uv run pytest tests/test_real_envs.py
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