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» Compositional Models for Reinforcement Learning
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ATAL
2010
Springer
13 years 8 months ago
PAC-MDP learning with knowledge-based admissible models
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
Marek Grzes, Daniel Kudenko
CORR
2002
Springer
100views Education» more  CORR 2002»
13 years 7 months ago
A neural model for multi-expert architectures
We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural fr...
Marc Toussaint
SLOGICA
2008
120views more  SLOGICA 2008»
13 years 7 months ago
Emergence of Information Transfer by Inductive Learning
We study a simple game theoretic model of information transfer which we consider to be a baseline model for capturing strategic aspects of epistemological questions. In particular,...
Simon M. Huttegger, Brian Skyrms
AAAI
2007
13 years 10 months ago
RETALIATE: Learning Winning Policies in First-Person Shooter Games
In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team firstperson shooter games. RETALIATE has three crucial chara...
Megan Smith, Stephen Lee-Urban, Hector Muño...
ML
2008
ACM
152views Machine Learning» more  ML 2008»
13 years 7 months ago
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
András Antos, Csaba Szepesvári, R&ea...