facebookresearch/end-to-end-negotiator

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1400 stars · 278 forks observed · 2026-08-28

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

A PyTorch research codebase from Facebook AI Research implementing end-to-end learning for negotiation dialogues, including models for hierarchical text generation and planning for strategic dialogue. It trains neural agents to negotiate in natural language via supervised learning, reinforcement learning self-play, and rollout-based planning, and ships with a dataset of 5,808 negotiation dialogues.

Use cases

• train a neural network to negotiate in natural language
• reproduce the Deal or No Deal negotiation dialogue paper
• run reinforcement learning self-play between dialogue agents
• get a dataset of negotiation dialogues for dialogue research
• experiment with hierarchical text generation and planning for strategic dialogue

When to choose

• you are researching negotiation dialogues, strategic dialogue, or dialogue-based RL and want the original reference implementation
• you need the accompanying negotiation dialogue dataset for experiments
• you want to study rollout-based planning combined with language generation in PyTorch

When to avoid

• you need a production-ready chatbot or dialogue system for real applications
• you want a maintained library with recent PyTorch support - the code targets PyTorch 1.0 and has not been updated since 2020
• you need multi-turn open-domain conversation rather than item-division negotiation

Facets

library · maturity maintenance

machine-learning deep-learning nlp reinforcement-learning chatbot deep-learning artificial-intelligence python pytorch negotiation-dialogue dialogue-systems research-code self-play facebook-ai-research dataset natural-language-processing research linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/end-to-end-negotiator")