Learning to Negotiate via Voluntary Commitment

Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian, Pascal Poupart
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:1459-1467, 2025.

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 \url{https://github.com/shuhui-zhu/DCL.}

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-zhu25b, title = {Learning to Negotiate via Voluntary Commitment}, author = {Zhu, Shuhui and Wang, Baoxiang and Subramanian, Sriram Ganapathi and Poupart, Pascal}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {1459--1467}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/zhu25b/zhu25b.pdf}, url = {https://proceedings.mlr.press/v258/zhu25b.html}, 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 \url{https://github.com/shuhui-zhu/DCL.}} }
Endnote
%0 Conference Paper %T Learning to Negotiate via Voluntary Commitment %A Shuhui Zhu %A Baoxiang Wang %A Sriram Ganapathi Subramanian %A Pascal Poupart %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-zhu25b %I PMLR %P 1459--1467 %U https://proceedings.mlr.press/v258/zhu25b.html %V 258 %X 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 \url{https://github.com/shuhui-zhu/DCL.}
APA
Zhu, S., Wang, B., Subramanian, S.G. & Poupart, P.. (2025). Learning to Negotiate via Voluntary Commitment. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:1459-1467 Available from https://proceedings.mlr.press/v258/zhu25b.html.

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