Principle:Farama Foundation Gymnasium Observation Wrapping

Knowledge Sources	Gymnasium Observation Wrappers OpenAI Gym
Domains	Reinforcement_Learning, Preprocessing
Last Updated	2026-02-15 03:00 GMT

Overview

A design pattern that transforms environment observations through composable wrapper layers without modifying the underlying environment implementation.

Description

Observation Wrapping applies the decorator pattern to transform observations between what the environment produces and what the agent receives. Common transformations include:

• Flattening: Converting Dict or Tuple observations to flat Box vectors for neural network input
• Normalization: Scaling observations to zero mean and unit variance
• Resizing: Downscaling image observations for efficiency
• Frame stacking: Concatenating consecutive frames for temporal context
• Type conversion: Converting between NumPy, JAX, and PyTorch arrays

Wrappers compose naturally: each wraps the previous, forming a chain where observations flow outward through transformations. The observation_space is updated at each layer to reflect the transformation.

Usage

Use observation wrapping when the raw environment observations need preprocessing for the agent. FlattenObservation is essential when using Dict or Tuple observation spaces with standard neural network architectures that expect flat vector inputs.

Theoretical Basis

Observation wrapping applies a function $f$ to the observation:

$o^{\prime }=f(o),o\in {𝒮},o^{\prime }\in f({𝒮})$

For flattening: $f:\text{Dict}(S_1,S_2,\dots )\to \text{Box}({\mathbb{ℝ}}^{d_1+d_2+\dots })$

Wrappers compose: $o^{″}=g(f(o))$ with the observation space updated accordingly.

Related Pages

Implemented By

• Implementation:Farama_Foundation_Gymnasium_FlattenObservation_Wrapper