A Study for Decentralized Cooperative Reinforcement Learning with Fairness Among Agents

Abstract

Equality among agents in multiagent reinforcement learning is an important issue in practical domains. In an existing study, a centralized solution method based on the approach of multi-objective reinforcement learning that reduces the action cost of each agent and improves the equality among them has been proposed. However, it applies a solution method based on a modified Q-learning on joint state- action space for all agents. Therefore, its state- action space is relatively large, and there are opportunities of decentralized methods that decompose the state-action space into multiple parts and integrate them via multiagent cooperation techniques. Toward the application of the existing method to decentralized approaches, we investigate a solution method that employ decomposed learning tables for partial joint state-action spaces of pairs of agents, and experimentally evaluate the influence of the proposed approach.