Multi-agent Relational Reinforcement Learning

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Multi-agent Relational Reinforcement Learning
In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far. In this paper we explore the powerful possibilities of using Relational Reinforcement Learning (RRL) in complex multi-agent coordination tasks. More prewe consider an abstract multi-state coordination problem, which can be considered as a variation and extension of repeated stateless Dispersion Games. Our approach shows that RRL allows to represent a complex state space in a multi-agent environment more compactly and allows for fast convergence of lear...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Authors Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice Bruynooghe
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