Reinforcement learning for decentralized planning under uncertainty
Landon Kraemer · Aquila Digital Community (University of Southern Mississippi) · 2013
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. But in real world scenarios, model parameters may not be known a priori, or may be difficult to specify. We propose to address these limitations with distributed reinforcement learning (RL). Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.