Efficient multi-agent reinforcement learning through automated supervision

Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision framework to speed up the convergence of MARL algorithms in a network of agents. The framework defines an organizational structure for automated supervision and a communication protocol for exchanging information between lower-level agents and higher-level supervising agents. racted states of lower-level agents travel upwards so that higher-level supervising agents generate a broader view of the state of the network. This broader view is used in creating supervisory information which is passed down the hierarchy. We present a generic extension to MARL algorithms that integrates supervisory information into the learning process, guiding agents' exploration of their stateaction space. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; I.2.11 [Artificial Intelligence]: Distributed A...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
• Keywords: Artificial Intelligence, ATAL 2008, Intelligent Agents, Lower-level Agents, MARL Algorithms