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ICTAI
2003
IEEE
14 years 3 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau
ICML
2002
IEEE
14 years 10 months ago
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...
Carlos Guestrin, Relu Patrascu, Dale Schuurmans
ICML
2005
IEEE
14 years 10 months ago
Bayesian sparse sampling for on-line reward optimization
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
UAI
2008
13 years 11 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
NCI
2004
185views Neural Networks» more  NCI 2004»
13 years 11 months ago
Hierarchical reinforcement learning with subpolicies specializing for learned subgoals
This paper describes a method for hierarchical reinforcement learning in which high-level policies automatically discover subgoals, and low-level policies learn to specialize for ...
Bram Bakker, Jürgen Schmidhuber