How can agents reliably commit to cooperative plans?
Learning to Negotiate via Voluntary Commitment
This paper introduces Markov Commitment Games (MCGs), a framework for multi-agent systems where agents can propose and commit to future actions, fostering cooperation in mixed-motive scenarios. A learning algorithm, Differentiable Commitment Learning (DCL), enables agents to learn effective commitment strategies through policy gradients. DCL considers joint actions and backpropagates through other agents' policies (or estimated policies in decentralized settings) for more accurate training. Key points for LLM-based multi-agent systems include the learnable commitment mechanism without explicit reward manipulation, the potential for improved cooperation in complex environments through DCL, and the ability to generalize across tasks without pre-defined rules or centralized control (decentralized DCL). The MCG framework, combined with DCL, offers a promising approach for building cooperative LLM-based multi-agent systems.