Learning to Negotiate via Voluntary Commitment

Shuhui Zhu; Baoxiang Wang; Sriram Ganapathi Subramanian; Pascal Poupart

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arXiv · 2503.03866

Learning to Negotiate via Voluntary Commitment

Shuhui Zhu·Baoxiang Wang·Sriram Ganapathi Subramanian·Pascal Poupart

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Abstract

The partial alignment and conflict of autonomous agents lead to mixed-motive scenarios in many real-world applications. However, agents may fail to cooperate in practice even when cooperation yields a better outcome. One well known reason for this failure comes from non-credible commitments. To facilitate commitments among agents for better cooperation, we define Markov Commitment Games (MCGs), a variant of commitment games, where agents can voluntarily commit to their proposed future plans. Based on MCGs, we propose a learnable commitment protocol via policy gradients. We further propose incentive-compatible learning to accelerate convergence to equilibria with better social welfare. Experimental results in challenging mixed-motive tasks demonstrate faster empirical convergence and higher returns for our method compared with its counterparts. Our code is available at https://github.com/shuhui-zhu/DCL.

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Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian, Pascal Poupart. 2025-03-05. Learning to Negotiate via Voluntary Commitment. https://arxiv.org/abs/2503.03866

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