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» Variational methods for Reinforcement Learning
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ECCV
2008
Springer
14 years 9 months ago
Local Regularization for Multiclass Classification Facing Significant Intraclass Variations
We propose a new local learning scheme that is based on the principle of decisiveness: the learned classifier is expected to exhibit large variability in the direction of the test ...
Lior Wolf, Yoni Donner
TSMC
2008
132views more  TSMC 2008»
13 years 7 months ago
Ensemble Algorithms in Reinforcement Learning
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and fin...
Marco A. Wiering, Hado van Hasselt
AAAI
2006
13 years 9 months ago
Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning
Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world proble...
Shimon Whiteson, Peter Stone
AAAI
2012
11 years 10 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
IWLCS
2005
Springer
14 years 1 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