Presentation information

General Session

General Session » GS-5 Agents

[1N1-GS-5] Agents: basic theory

Tue. Jun 14, 2022 10:00 AM - 11:40 AM Room N (Room 501)

座長:高野 諒(立命館大学)[遠隔]

11:20 AM - 11:40 AM

[1N1-GS-5-05] A Study for Decentralized Cooperative Reinforcement Learning with Fairness Among Agents

〇Toshihiro Matsui1 (1. Nagoya Institute of Technology)


Keywords:multiagent system, reinforcement learning, cooperative problem solving

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.

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