Heuristic:Farama Foundation Gymnasium Action Space Normalization Tip
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Optimization |
| Last Updated | 2026-02-15 03:00 GMT |
Overview
Custom environments with continuous (Box) action spaces should use symmetric, normalized ranges ([-1, 1] or [0, 1]) for stable RL training.
Description
Gymnasium's `check_env()` validator warns when a Box action space is not symmetric and normalized. Most RL algorithms (PPO, SAC, TD3) use Gaussian policies whose output is naturally centered around zero with unit variance. When action spaces have asymmetric or large ranges (e.g., [0, 100] or [-50, 200]), the policy network must learn both the correct offset and scale, which slows training and can cause instability. Normalizing action spaces to [-1, 1] and applying the inverse transform in the environment is a widely-adopted best practice.
Usage
Use this heuristic when designing custom environments with continuous (Box) action spaces, or when debugging slow convergence in RL training with continuous actions. The `RescaleAction` wrapper can apply this normalization to existing environments without modifying their source code.
The Insight (Rule of Thumb)
- Action: Define Box action spaces with `low=-1.0, high=1.0` (or `low=0.0, high=1.0` for non-negative actions). Apply inverse scaling inside the environment's `step()` method.
- Value: Range `[-1, 1]` is preferred. Range `[0, 1]` acceptable for non-negative domains.
- Trade-off: Requires an extra mapping step in the environment, but dramatically improves policy learning stability and convergence speed.
- Wrapper alternative: Use `gymnasium.wrappers.RescaleAction` to normalize existing environments: `RescaleAction(env, min_action=-1.0, max_action=1.0)`.
Reasoning
The env_checker validates this because RL algorithms that use Gaussian policies (PPO, SAC, TD3, A2C) sample actions from a distribution centered on the policy output. When the action space is [-1, 1], the initial random policy naturally explores the full action range. With asymmetric or large ranges, the initial policy may only explore a small fraction of the space, leading to poor sample efficiency.
Additionally, observation and action space bounds of `-inf` or `inf` are flagged as "probably too low/high" because unbounded spaces provide no useful information to the policy about valid action ranges.
Code Evidence
Action space normalization check from `gymnasium/utils/env_checker.py:326-342`:
# Check that the Box space is normalized
if space_type == "action":
if len(space.shape) == 1: # for vector boxes
if (
np.any(
np.logical_and(
space.low != np.zeros_like(space.low),
np.abs(space.low) != np.abs(space.high),
)
)
or np.any(space.low < -1)
or np.any(space.high > 1)
):
logger.warn(
"For Box action spaces, we recommend using a symmetric and normalized space (range=[-1, 1] or [0, 1]). "
"See https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html for more information."
)
Infinity bounds warning from `gymnasium/utils/env_checker.py:316-323`:
if np.any(np.equal(space.low, -np.inf)):
logger.warn(
f"A Box {space_type} space minimum value is -infinity. This is probably too low."
)
if np.any(np.equal(space.high, np.inf)):
logger.warn(
f"A Box {space_type} space maximum value is infinity. This is probably too high."
)