The Self Organization of Context for Learning in MultiAgent Games

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The Self Organization of Context for Learning in MultiAgent Games
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, tile coding with linear function approximation has been widely used to circumvent the curse of dimensionality, but it suffers from the drawback that human-guided identification of features is required to create effective tilings. The challenge is to find tilings that preserve the context necessary to evaluate the value of a state-action pair while limiting memory requirements. The technique presented in this paper addresses the difficulty of identifying context in highdimensional domains. We have chosen RoboCup simulated soccer as a domain because its high-dimensional continuous state space makes it a formidable challenge for reinforcement learning algorithms. Using self-organizing maps and reinforcement learning in a two-pass process, our technique scales to large state spaces without requiring a large amount ...
Christopher D. White, Dave Brogan
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Authors Christopher D. White, Dave Brogan
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