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» Model-based function approximation in reinforcement learning
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ICML
2008
IEEE
16 years 17 days ago
Reinforcement learning in the presence of rare events
We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these ev...
Jordan Frank, Shie Mannor, Doina Precup
JMLR
2006
153views more  JMLR 2006»
14 years 11 months ago
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of c...
Jelle R. Kok, Nikos A. Vlassis
NIPS
1998
15 years 1 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
ATAL
2008
Springer
15 years 1 months ago
Adaptive Kanerva-based function approximation for multi-agent systems
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
Cheng Wu, Waleed Meleis
ECML
2006
Springer
15 years 3 months ago
Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Sébastien Jodogne, Cyril Briquet, Justus H....