Principle:Farama Foundation Gymnasium Episode Time Limiting

Knowledge Sources	Gymnasium Wrappers Sutton and Barto RL
Domains	Reinforcement_Learning, Episode_Management
Last Updated	2026-02-15 03:00 GMT

Overview

A mechanism that enforces maximum episode duration by truncating episodes that exceed a step limit, distinct from MDP-defined termination.

Description

Episode Time Limiting imposes an external constraint on episode length independent of the environment's internal dynamics. After a specified number of steps, the episode is truncated (not terminated), signaling to the agent that the episode ended due to time, not because a terminal MDP state was reached.

This distinction is critical for RL algorithms:

• Terminated: The value of the terminal state is 0 (no future rewards possible)
• Truncated: The value of the state may be non-zero (the agent was artificially stopped)

Correct handling prevents bias in value function estimation. Time limits are typically set via max_episode_steps during environment registration.

Usage

Use time limiting for environments where episodes could run indefinitely without intervention. Most registered Gymnasium environments include a default max_episode_steps that is automatically applied by gymnasium.make().

Theoretical Basis

Time truncation at step $T_{max}$:

${\text{truncated}}_t=\{\begin{array}{ll}\text{True}& \text{if }t\ge T_{max}\\ \text{False}& \text{otherwise}\end{array}$

For correct bootstrapping in temporal-difference learning:

$V(s_t)\leftarrow \{\begin{array}{ll}0& \text{if terminated}\\ V(s_{t+1})& \text{if truncated (bootstrap)}\end{array}$

Related Pages

Implemented By

• Implementation:Farama_Foundation_Gymnasium_TimeLimit_Wrapper