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» Reinforcement learning in a nutshell
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CIG
2005
IEEE
15 years 11 months ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes
TSMC
2008
76views more  TSMC 2008»
15 years 5 months ago
Improved Adaptive-Reinforcement Learning Control for Morphing Unmanned Air Vehicles
This paper presents an improved Adaptive
John Valasek, James Doebbler, Monish D. Tandale, A...
IWLCS
2005
Springer
15 years 11 months ago
Counter Example for Q-Bucket-Brigade Under Prediction Problem
Aiming to clarify the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of ...
Atsushi Wada, Keiki Takadama, Katsunori Shimohara
139
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ICML
2006
IEEE
16 years 6 months ago
PAC model-free reinforcement learning
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...
169
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AGENTS
2001
Springer
15 years 10 months ago
Using background knowledge to speed reinforcement learning in physical agents
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...
Daniel G. Shapiro, Pat Langley, Ross D. Shachter