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Argumentation Accelerated Reinforcement Learning for RoboCup Keepaway-Takeaway

  • Yang Gao
  • Francesca Toni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8306)

Abstract

Multi-Agent Learning (MAL) is a complex problem, especially in real-time systems where both cooperative and competitive learning are involved. We study this problem in the RoboCup Soccer Keepaway-Takeaway game and propose Argumentation Accelerated Reinforcement Learning (AARL) for this game. AARL incorporates heuristics, represented by arguments in Value-Based Argumentation, into Reinforcement Learning (RL) by using Heuristically Accelerated RL techniques. We empirically study for a specific setting of the Keepaway-Takeaway game the suitability of AARL, in comparison with standard RL and hand-coded strategies, to meet the challenges of MAL.

Keywords

Reinforcement Learn Multiagent System Markov Decision Process Argumentation Framework Strict Partial Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yang Gao
    • 1
  • Francesca Toni
    • 1
  1. 1.Department of ComputingImperial College LondonUK

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