IteratedMatchingPennies

Description

IteratedMatchingPennies is an environment for evaluating agents on mixed strategy equilibrium play in a classic game theory scenario. This environment wraps the IteratedMatchingPennies implementation from TextArena, a framework for text-based game environments.

Capabilities

• Game theory and mixed strategy reasoning
• Pattern recognition and exploitation
• Adaptive strategy development
• Competitive gameplay against an LLM opponent

Compute Requirements

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

License

MIT.

Tasks

There are two splits: train (150 tasks) and test (150 tasks). Each split contains 50 tasks across each of 3 variants:

• IteratedMatchingPennies-v0
• IteratedMatchingPennies-v0-train
• IteratedMatchingPennies-v0-raw

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:

• choose_side(choice): Choose heads or tails for this round.

Time Horizon

IteratedMatchingPennies is a multi-turn environment.

Environment Difficulty

Medium - requires understanding of mixed strategy equilibria and the ability to balance randomness with opponent modeling to maximize matching successes.

Other Environment Requirements

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

Safety

Agents in IteratedMatchingPennies interact only with a game theory simulation 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}
}