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» Reinforcement learning in a nutshell
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CIG
2005
IEEE
14 years 2 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»
13 years 8 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
14 years 2 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
ICML
2006
IEEE
14 years 9 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...
AGENTS
2001
Springer
14 years 1 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