Learning Multi-agent Coordination Through Connectivity-driven Communication

Abstract

In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents' communication skills: they must be able to encode the information received from the environment and learn how to share it with other agents as required by the task at hand. We present a deep reinforcement learning approach, Connectivity Driven Communication (CDC), that facilitates the emergence of multi-agent collaborative behaviour only through experience. The agents are modelled as nodes of a weighted graph whose state-dependent edges encode pair-wise messages that can be exchanged. We introduce a graph-dependent attention mechanisms that controls how the agents' incoming messages are weighted. This mechanism takes into full account the current state of the system as represented by the graph, and builds upon a diffusion process that captures how the information flows on the graph. The graph topology is not assumed to be known a priori, but depends dynamically on the agents' o

Stats

• citations13
• S2 citations—
• github stars0
• HF likes0
• heat score8.60
• arxiv keypesce2020learning

Related papers

• Research On Multi-agent Communication And Collaborative Decision-making Based On Deep Reinforcement Learning (2023)0.00
• Counterfactual Multi-agent Reinforcement Learning With Graph Convolution Communication (2020)0.00
• Learning-based Physical Layer Communications For Multi-agent Collaboration (2018)9.59
• AC2C: Adaptively Controlled Two-hop Communication For Multi-agent Reinforcement Learning (2023)0.00
• Learning Practical Communication Strategies In Cooperative Multi-agent Reinforcement Learning (2022)0.00
• GCS: Graph-based Coordination Strategy For Multi-agent Reinforcement Learning (2022)0.00
• Improving Coordination In Small-scale Multi-agent Deep Reinforcement Learning Through Memory-driven Communication (2019)12.25
• Learning To Schedule Communication In Multi-agent Reinforcement Learning (2019)0.00