학술논문
Multi-Agent Deep Reinforcement Learning with Multi-Branch Networks Considering Interaction / インタラクションを考慮したマルチブランチネットワークによる深層強化学習
Document Type
Journal Article
Author
Source
Proceedings of the Annual Conference of JSAI. 2020, :2
Subject
Language
Japanese
Abstract
When multiple agents are in the same environment, collisions may happen with each other. Because the agents consider their own interests or have a negative effect on other agents. In situation that happens these deadlocks, agents should select the action considering other agents based on multi-agent reinforcement learning which train multiple agents simultaneously. In this paper, we propose the method that trains multiple agents in a network with multi-branch network for this problem. It is possible to train an interaction between agents. In experiment, we build the environment that happens the deadlock between self-driving cars and compare with independent network of each agent. Moreover, we show the behavior of the agent in a deadlock situation.