Reinforcement Learning in Decentralized Stochastic Control Systems with Partial History Sharing
published: July 28, 2015, recorded: June 2015, views: 2135
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this paper, we are interested in systems with multiple agents that wish to cooperate in order to accomplish a common task while a) agents have different information (decentralized information) and b) agents do not know the complete model of the system i.e., they may only know the partial model or may not know the model at all. The agents must learn the optimal strategies by interacting with their environment i.e., by multi-agent Reinforcement Learning (RL). The presence of multiple agents with different information makes multi-agent (decentralized) reinforcement learning conceptually more difficult than single-agent (centralized) reinforcement learning. We propose a novel multi-agent reinforcement learning algorithm that learns epsilon-team-optimal solution for systems with partial history sharing information structure, which encompasses a large class of multi-agent systems including delayed sharing, control sharing, mean field sharing, etc. Our approach consists of two main steps as follows: 1) the multiagent (decentralized) system is converted to an equivalent single-agent (centralized) POMDP (Partial Observable Markov Decision Process) using the common information approach of Nayyar et al, TAC 2013, and 2) based on the obtained POMDP, an approximate RL algorithm is constructed using a novel methodology. We show that the performance of the RL strategy converges to the optimal performance exponentially fast. We illustrate the proposed approach and verify it numerically by obtaining a multi-agent Q-learning algorithm for two-user Multi Access Broadcast Channel (MABC) which is a benchmark example for multi-agent systems.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !