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Planning and Learning For Decentralized MDPs With Event Driven RewardsAuthors: Tarun Gupta,Akshat Kumar,Praveen Paruchuri Conference: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018 2018) Location New Orleans, USA Date: 2018-02-02 Report no: IIIT/TR/2018/120 AbstractDecentralized (PO)MDPs provide a rigorous framework for sequential multiagent decision making under uncertainty. However, their high computational complexity limits the practical impact. To address scalability and real-world impact, we focus on settings where a large number of agents primarily interact through complex joint-rewards that depend on their entire histories of states and actions. Such history-based rewards encapsulate the notion of events or tasks such that the team reward is given only when the joint-task is completed. Algorithmically, we contribute — 1) A nonlinear programming (NLP) formulation for such event-based planning model; 2) A probabilistic inference based approach that scales much better than NLP solvers for a large number of agents; 3) A policy gradient based multiagent reinforcement learning approach that scales well even for exponential state-spaces. Our inference and RL-based advances enable us to solve a large real-world multiagent coverage problem modeling schedule coordination of agents in a real urban subway network where other approaches fail to scale. Full paper: pdf Centre for Visual Information Technology |
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