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Article overview
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DM$^2$: Distributed Multi-Agent Reinforcement Learning for Distribution Matching | | Caroline Wang
; Ishan Durugkar
; Elad Liebman
; Peter Stone
; | | Date: |
1 Jun 2022 | | Abstract: | Current approaches to multi-agent cooperation rely heavily on centralized
mechanisms or explicit communication protocols to ensure convergence. This
paper studies the problem of distributed multi-agent learning without resorting
to explicit coordination schemes. The proposed algorithm (DM$^2$) leverages
distribution matching to facilitate independent agents’ coordination. Each
individual agent matches a target distribution of concurrently sampled
trajectories from a joint expert policy. The theoretical analysis shows that
under some conditions, if each agent optimizes their individual distribution
matching objective, the agents increase a lower bound on the objective of
matching the joint expert policy, allowing convergence to the joint expert
policy. Further, if the distribution matching objective is aligned with a joint
task, a combination of environment reward and distribution matching reward
leads to the same equilibrium. Experimental validation on the StarCraft domain
shows that combining the reward for distribution matching with the environment
reward allows agents to outperform a fully distributed baseline. Additional
experiments probe the conditions under which expert demonstrations need to be
sampled in order to outperform the fully distributed baseline. | | Source: | arXiv, 2206.00233 | | Services: | Forum | Review | PDF | Favorites | |
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