Othello

Description

Othello is an environment for evaluating agents on the classic board game also known as Reversi. This environment wraps the Othello implementation from TextArena, a framework for text-based game environments.

Capabilities

• Strategic board game reasoning
• Positional evaluation and tactical planning
• Long-term strategic thinking with cascading effects
• Two-player competitive gameplay against an LLM opponent

Compute Requirements

Othello does not require a sandbox. It has minimal compute requirements.

License

MIT.

Tasks

There are two splits: train (50 tasks) and test (50 tasks). Each split contains 50 tasks across each of 1 variants:

• Othello-v0

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:

• place_disc(row, col): Place your disc on the board at the given row and column (both 0-7).

Time Horizon

Othello is a multi-turn environment.

Environment Difficulty

Moderate to Hard. Othello requires strategic thinking about board position, understanding corner and edge control, and anticipating how moves will cascade through the board.

Other Environment Requirements

This environment requires an OpenAI API key (passed via secrets) to power the LLM opponent.

Safety

Agents in Othello 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}
}