Multi-agent reinforcement learning as a rehearsal for decentralized planning

NEUROCOMPUTING

卷:190

Article

Kraemer, Landon¹  Banerjee, Bikramjit¹ 

[1] Univ So Mississippi, Sch Comp, Hattiesburg, MS 39406 USA

关键词: Multi-agent reinforcement learning;    Decentralized planning;   

DOI  :  10.1016/j.neucom.2016.01.031

来源: Elsevier

【 摘 要 】

Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some, practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features. We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsal based learners, and demonstrate fast, (near) optimal performance on many existing benchmark Dec-POMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR's policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free