Principle:LaurentMazare Tch rs Gym Environment Interface

Knowledge Sources	LaurentMazare_Tch_rs OpenAI Gym
Domains	Reinforcement Learning, Software Architecture
Last Updated	2026-02-08 00:00 GMT

Overview

The Gym environment interface defines a standardized protocol for reinforcement learning environments, providing a uniform API for agent-environment interaction regardless of the underlying task.

Description

The Gym environment interface establishes a universal contract between reinforcement learning agents and environments. This abstraction enables agents to be developed independently of specific tasks and transferred across different environments with minimal code changes. The interface consists of:

• reset(): Initializes or reinitializes the environment to a starting state and returns the initial observation. This is called at the beginning of each episode and whenever an episode terminates. The observation is a structured representation of the environment state visible to the agent (e.g., pixel arrays, joint angles, board positions).

• step(action): Advances the environment by one time step given an action selected by the agent. Returns a tuple of four elements:
  • observation: The new state observation after the action is applied.
  • reward: A scalar signal indicating the immediate desirability of the transition. The agent's objective is to maximize cumulative reward.
  • done: A boolean flag indicating whether the episode has terminated (either by reaching a goal state, failing, or exceeding a time limit).
  • info: An optional dictionary of auxiliary diagnostic information not used for training.

• action_space: A specification of the set of valid actions. For discrete action spaces, this is a finite set of integers. For continuous action spaces, this is a bounded region of ${\mathbb{ℝ}}^n$.

• observation_space: A specification of the shape, dtype, and bounds of observations. This metadata enables agents to configure their input layers and preprocessing accordingly.

Usage

The Gym interface is used whenever building or interacting with reinforcement learning environments. It is the de facto standard adopted by virtually all RL frameworks and benchmarks, enabling reproducible research and modular agent design.

Theoretical Basis

Markov Decision Process (MDP) Formalization:

The Gym interface implements the agent-environment loop of a Markov Decision Process defined by the tuple $(S,A,P,R,\gamma )$:

• $S$: State space (observations are partial or full state representations)
• $A$: Action space
• $P(s^{\prime }|s,a)$: Transition dynamics (encapsulated within the environment)
• $R(s,a,s^{\prime })$: Reward function
• $\gamma \in [0,1]$: Discount factor

Agent-Environment Loop:

s_0 := environment.reset()
for t = 0, 1, 2, ...:
    a_t := agent.select_action(s_t)
    s_{t+1}, r_t, done_t, info_t := environment.step(a_t)
    agent.observe(s_t, a_t, r_t, s_{t+1}, done_t)
    if done_t:
        s_0 := environment.reset()

Space Specifications:

Discrete space: $A=\{0,1,\dots ,n-1\}$

Continuous (Box) space: $A=\{x\in {\mathbb{ℝ}}^d:{\text{low}}_i\le x_i\le {\text{high}}_i\}$

Multi-discrete space: $A=\{0,\dots ,n_1-1\}\times \{0,\dots ,n_2-1\}\times \cdots$

Episode Return:

The objective of an agent interacting through this interface is to maximize the expected discounted return:

$G_t=\sum _{k=0}^{\mathrm{\infty }}{\gamma }^k{r}_{t+k}$

which for finite episodes (where done becomes true at step $T$) reduces to:

$G_t=\sum _{k=0}^{T-t-1}{\gamma }^k{r}_{t+k}$

Related Pages

• Implementation:LaurentMazare_Tch_rs_GymEnv