DARL1N: Distributed multi-Agent Reinforcement Learning with One-hop Neighbors
Baoqian Wang,Junfei Xie,Nikolay Atanasov,Baoqian Wang,Junfei Xie,Nikolay Atanasov
Multi-agent reinforcement learning (MARL) meth-ods face a curse of dimensionality in the policy and value function representations as the number of agents increases. The development of distributed or parallel training techniques is also hindered by the global coupling among the agent dynamics, requiring simultaneous state transitions. This paper introduces Distributed multi-Agent Reinforcement Lea...