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» Reinforcement Learning: An Introduction
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NIPS
2001
13 years 9 months ago
Reinforcement Learning with Long Short-Term Memory
This paper presents reinforcement learning with a Long ShortTerm Memory recurrent neural network: RL-LSTM. Model-free RL-LSTM using Advantage learning and directed exploration can...
Bram Bakker
NIPS
2001
13 years 9 months ago
Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning
We address two open theoretical questions in Policy Gradient Reinforcement Learning. The first concerns the efficacy of using function approximation to represent the state action ...
Gregory Z. Grudic, Lyle H. Ungar
BROADNETS
2007
IEEE
13 years 12 months ago
Reinforcement learning based routing in all-optical networks with physical impairments
Abstract-- We present and evaluate a reinforcement learningbased RWA algorithm for all-optical networks subject to physical impairments. The technique is suitable for decentralized...
Yvan Pointurier, Fariba Heidari
ICML
2009
IEEE
14 years 8 months ago
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an ...
Carlos Diuk, Lihong Li, Bethany R. Leffler
IJAIT
2008
146views more  IJAIT 2008»
13 years 8 months ago
Learning to Behave in Space: a Qualitative Spatial Representation for Robot Navigation with Reinforcement Learning
ion mechanism to create a representation of space consisting of the circular order of detected landmarks and the relative position of walls towards the agent's moving directio...
Lutz Frommberger