Checkers
Checkers
Description
Checkers is an ORS environment for evaluating agents on playing the classic Checkers game against an LLM opponent. This environment wraps the Checkers implementation from TextArena, a framework for text-based game environments.
Capabilities
- Strategic piece movement and jump mechanics
- Multi-jump capture sequences and chain planning
- King promotion and bidirectional movement reasoning
- Competitive two-player gameplay against an LLM opponent
Compute Requirements
Checkers does not require a sandbox. It has minimal compute requirements.
License
MIT.
Tasks
There are two splits: train (300 tasks) and test (300 tasks). Each split contains 50 tasks across each of 6 variants:
- Checkers-v0
- Checkers-v0-long
- Checkers-v0-long-raw
- Checkers-v0-long-train
- Checkers-v0-raw
- Checkers-v0-train
Each task is seeded for reproducibility.
Reward Structure
This is a sparse reward environment. Rewards are mapped from TextArena's native range of {-1, 0, 1} to {0.0, 0.5, 1.0} via (raw + 1) / 2.
We do not use LLM graders for this environment; reward is determined programmatically.
Data
Game state is generated procedurally by the TextArena engine using seeded randomness. No external data files are required.
Tools
Agents are given a single tool:
move_checker(from_row, from_col, to_row, to_col): Move a checker piece from one position to another. Provide from_row, from_col, to_row, to_col (0-indexed).
Time Horizon
Checkers is a multi-turn environment.
Environment Difficulty
Medium
Other Environment Requirements
This environment requires an OpenAI API key (passed via secrets) to power the LLM opponent.
Safety
Agents in Checkers interact only with a board game and have no access to external systems, the internet, or sensitive data. The environment does not present safety risks.
Citations
@software{textarena2024,
author = {Guertler, Leon and Banting, Wilfried and Pignatelli, Eduardo},
title = {TextArena},
year = {2024},
publisher = {GitHub},
url = {https://github.com/LeonGuertler/TextArena}
}