Frequency adjusted multi-agent Q-learning

Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such systems are ubiquitous in many domains, including auctions in economics, swarm robotics in computer science, and politics in social sciences. Multi-agent learning is inherently more complex than single-agent learning and has a relatively thin theoretical framework supporting it. Recently, multi-agent learning dynamics have been linked to evolutionary game theory, allowing the interpretation of learning as an evolution of competing policies in the mind of the learning agents. The dynamical system from evolutionary game theory that has been linked to Q-learning predicts the expected behavior of the learning agents. Closer analysis however allows for two interesting observations: the predicted behavior is not always the same as the actual behavior, and in case of deviation, the predicted behavior is more desirabl...

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Michael Kaisers, Karl Tuyls
• Keywords: ATAL 2010, Evolutionary Game Theory, Intelligent Agents, Multi-agent Learning, Multi-agent Learning Dynamics