Multi-Agent Cross-Entropy Method with Monotonic Nonlinear Critic Decomposition
This addresses a key bottleneck in multi-agent systems for applications like robotics or gaming, though it appears incremental as it builds on prior value decomposition approaches.
The paper tackles the centralized-decentralized mismatch in cooperative multi-agent reinforcement learning by proposing the multi-agent cross-entropy method with monotonic nonlinear critic decomposition, which outperforms state-of-the-art methods on benchmarks.
Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution (CTDE), where centralized critics leverage global information to guide decentralized actors. However, centralized-decentralized mismatch (CDM) arises when the suboptimal behavior of one agent degrades others' learning. Prior approaches mitigate CDM through value decomposition, but linear decompositions allow per-agent gradients at the cost of limited expressiveness, while nonlinear decompositions improve representation but require centralized gradients, reintroducing CDM. To overcome this trade-off, we propose the multi-agent cross-entropy method (MCEM), combined with monotonic nonlinear critic decomposition (NCD). MCEM updates policies by increasing the probability of high-value joint actions, thereby excluding suboptimal behaviors. For sample efficiency, we extend off-policy learning with a modified k-step return and Retrace. Analysis and experiments demonstrate that MCEM outperforms state-of-the-art methods across both continuous and discrete action benchmarks.