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ICML
2008
IEEE
14 years 8 months ago
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
We show that linear value-function approximation is equivalent to a form of linear model approximation. We then derive a relationship between the model-approximation error and the...
Ronald Parr, Lihong Li, Gavin Taylor, Christopher ...
ICML
1996
IEEE
14 years 8 months ago
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
Research in reinforcementlearning (RL)has thus far concentrated on two optimality criteria: the discounted framework, which has been very well-studied, and the averagereward frame...
Sridhar Mahadevan
ICES
2003
Springer
125views Hardware» more  ICES 2003»
14 years 1 months ago
Evolving Reinforcement Learning-Like Abilities for Robots
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
Jesper Blynel
ICML
2006
IEEE
14 years 8 months ago
Qualitative reinforcement learning
When the transition probabilities and rewards of a Markov Decision Process are specified exactly, the problem can be solved without any interaction with the environment. When no s...
Arkady Epshteyn, Gerald DeJong
ICML
2000
IEEE
14 years 8 months ago
Reinforcement Learning in POMDP's via Direct Gradient Ascent
This paper discusses theoretical and experimental aspects of gradient-based approaches to the direct optimization of policy performance in controlled ??? ?s. We introduce ??? ?, a...
Jonathan Baxter, Peter L. Bartlett