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Successor Features Based Multi-Agent RL for Event-Based Decentralized MDPsAuthors: Tarun Gupta,Akshat Kumar,Praveen Paruchuri Conference: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19 2019) Location Honolulu, Hawaii, USA Date: 2019-01-27 Report no: IIIT/TR/2019/8 AbstractDecentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decision-making 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 Representtion) 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. Full paper: pdf Centre for Visual Information Technology |
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