Year: 2021
Type: preprint
Abstract: This paper establishes directionality reinforcement learning (DRL) technique to propose the complete decentralized multi-agent reinforcement learning method which can achieve cooperation based on each agent's learning: no communication and no observation. Concretely, DRL adds the direction "agents have to learn to reach the farthest goal among reachable ones" to learning agents to operate the agents cooperatively. Furthermore, to investigate the effectiveness of the DRL, this paper compare Q-learning agent with DRL with previous learning agent in maze problems. Experimental results derive that (1) DRL performs better than the previous method in terms of the spending time, (2) the direction makes agents learn yielding action for others, and (3) DRL suggests achieving multiagent learning with few costs for any number of agents.
Source: arXiv (Cornell University)
Authors: Uwano, Fumito, Takadama, Keiki
Institutions: Okayama University, University of Electro-Communications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: 通信無し強化学習エージェント群による動的環境への追従
OpenAlex ID: W3205683646