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2008
Springer

Efficient multi-agent reinforcement learning through automated supervision

8 years 4 months ago
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...
Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
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