Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2019

Abstract

Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty. However, their computational complexity limits the practical impact. To address this, we focus on a class of Dec-MDPs consisting of independent collaborating agents that are tied together through a global reward function that depends upon their entire histories of states and actions to accomplish joint tasks. To overcome scalability barrier, our main contributions are: (a) We propose a new actor-critic based Reinforcement Learning (RL) approach for event-based Dec-MDPs using successor features (SF) which is a value function representation that decouples the dynamics of the environment from the rewards; (b) We then present Dec-ESR (Decentralized Event based Successor Representation) which generalizes learning for event-based Dec-MDPs using SF within an end-to-end deep RL framework; (c) We also show that Dec-ESR allows useful transfer of information on related but different tasks, hence bootstraps the learning for faster convergence on new tasks; (d) For validation purposes, we test our approach on a large multi-agent coverage problem which models schedule coordination of agents in a real urban subway network and achieves better quality solutions than previous best approaches

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

AAAI Conference on Artificial Intelligence (AAAI)

First Page

6054

Last Page

6061

Identifier

10.1609/aaai.v33i01.33016054

City or Country

Hawaii

Additional URL

https://doi.org/10.1609/aaai.v33i01.33016054

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