facebookresearch/diplomacy_cicero

Health v2 · maintenance only

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Adoption not part of the score

1428 stars · 168 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Research code and model checkpoints for Cicero and Diplodocus, AI agents that play the board game Diplomacy at human level by combining language models with strategic planning and reinforcement learning. Built on ParlAI with Python training code and C++ components for search.

Use cases

• reproduce the Cicero Diplomacy AI agent from the Science paper
• train reinforcement learning agents for no-press Diplomacy
• study language model negotiation dialogue in games
• run self-play RL training for strategic board games
• benchmark strategic planning agents against human-level play
• fine-tune language models for game negotiation

When to choose

• you want to reproduce or extend published Diplomacy AI research
• you need checkpoints and training code for language-model-based game agents
• you're researching combining LLMs with planning and RL

When to avoid

• you want a ready-to-play Diplomacy game client
• you need a general-purpose game AI framework
• you lack GPU resources or ML research experience

Facets

library · maturity maintenance

machine-learning deep-learning reinforcement-learning nlp llm-training agent-framework game-engine simulation artificial-intelligence reinforcement-learning python game-ai diplomacy language-models strategic-reasoning research-code facebook-research negotiation natural-language-processing game-development research linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/diplomacy_cicero")