ThreePlayerGOPS is an environment for evaluating agents on strategic card play in the Game of Pure Strategy. This environment wraps the ThreePlayerGOPS implementation from TextArena, a framework for text-based game environments.
- Strategic card value assessment
- Anticipating opponent behavior
- Resource management across multiple rounds
- Simultaneous bidding and tie-breaking strategies
ThreePlayerGOPS does not require a sandbox. It has minimal compute requirements.
MIT.
There are two splits: train (150 tasks) and test (150 tasks). Each split contains 50 tasks across each of 3 variants:
- ThreePlayerGOPS-v0
- ThreePlayerGOPS-v0-train
- ThreePlayerGOPS-v0-raw
Each task is seeded for reproducibility.
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.
Game state is generated procedurally by the TextArena engine using seeded randomness. No external data files are required.
Agents are given a single tool:
play_card(card): Play a card from your hand. Use values like 'A', '2', '3', ..., '10', 'J', 'Q', 'K'.
ThreePlayerGOPS is a multi-turn environment.
Medium. Agents must balance immediate gains against future opportunities and anticipate opponent strategies in a simultaneous-play setting with 13 rounds.
This environment requires an OpenAI API key (passed via secrets) to power the LLM opponents.
Agents in ThreePlayerGOPS interact only with a strategic card game and have no access to external systems, the internet, or sensitive data. The environment does not present safety risks.
@software{textarena2024,
author = {Guertler, Leon and Banting, Wilfried and Pignatelli, Eduardo},
title = {TextArena},
year = {2024},
publisher = {GitHub},
url = {https://github.com/LeonGuertler/TextArena}
}