SpiteAndMalice

Description

SpiteAndMalice is an environment for evaluating agents on the competitive card game Spite and Malice. This environment wraps the SpiteAndMalice implementation from TextArena, a framework for text-based game environments.

Capabilities

• Strategic card game playing with sequential building
• Multi-action decision making (draw, play, discard)
• Card sequencing and pile management
• Competitive gameplay against LLM opponents

Compute Requirements

SpiteAndMalice 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:

• SpiteAndMalice-v0
• SpiteAndMalice-v0-train
• SpiteAndMalice-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:

• take_action(action): Submit your action in Spite and Malice. Examples: 'draw', 'play K♣ 0', 'discard 3♦ 1'.

Time Horizon

SpiteAndMalice is a multi-turn environment.

Environment Difficulty

Medium to Hard - requires strategic card sequencing and planning.

Other Environment Requirements

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

Safety

Agents in SpiteAndMalice interact only with a card 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}
}