Title
Multi-agent reinforcement learning as a rehearsal for decentralized planning.
Abstract
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.
Year
DOI
Venue
2016
10.1016/j.neucom.2016.01.031
Neurocomputing
Keywords
Field
DocType
Multi-agent reinforcement learning,Decentralized planning
Weak convergence,Decentralized planning,Computer science,A priori and a posteriori,Markov decision process,Artificial intelligence,Solver,Machine learning,Scalability,Reinforcement learning,Computation
Journal
Volume
Issue
ISSN
190
C
0925-2312
Citations 
PageRank 
References 
19
0.87
17
Authors
2
Name
Order
Citations
PageRank
Landon Kraemer18910.03
Bikramjit Banerjee228432.63