Decentralized Multi-agent Reinforcement Learning Algorithm Using A Cluster-synchronized Laser Network

Abstract

Multi-agent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. We propose a photonic-based decision-making algorithm to address one of the most fundamental problems in MARL, called the competitive multi-armed bandit (CMAB) problem. Our numerical simulations demonstrate that chaotic oscillations and cluster synchronization of optically coupled lasers, along with our proposed decentralized coupling adjustment, efficiently balance exploration and exploitation while facilitating cooperative decision-making without explicitly sharing information among agents. Our study demonstrates how decentralized reinforcement learning can be achieved by exploiting complex physical processes controlled by simple algorithms.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykotoku2024decentralized

Related papers

• Fully Decentralized Multi-agent Reinforcement Learning With Networked Agents (2018)0.00
• Decentralized Multi-agent Reinforcement Learning With Networked Agents: Recent Advances (2019)0.00
• Scalable Photonic Reinforcement Learning By Time-division Multiplexing Of Laser Chaos (2018)13.05
• Local Advantage Networks For Cooperative Multi-agent Reinforcement Learning (2021)0.00
• Mean-field Multi-agent Reinforcement Learning: A Decentralized Network Approach (2021)0.00
• Multi-agent Reinforcement Learning In Stochastic Networked Systems (2020)0.00
• Bandit Approach To Conflict-free Multi-agent Q-learning In View Of Photonic Implementation (2022)0.00
• Laser Learning Environment: A New Environment For Coordination-critical Multi-agent Tasks (2024)0.00