Learning to Negotiate via Voluntary Commitment
This addresses cooperation failures in autonomous agents for applications like multi-agent systems, though it appears incremental as it builds on existing commitment game frameworks.
The paper tackles the problem of non-credible commitments hindering cooperation in mixed-motive scenarios by introducing Markov Commitment Games (MCGs) and a learnable commitment protocol, achieving faster convergence and higher returns in experiments.
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.