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12 years 9 months ago
Robust Bayesian reinforcement learning through tight lower bounds
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinfo...
Christos Dimitrakakis
JACIII
2006
97views more  JACIII 2006»
13 years 11 months ago
Opposition-Based Reinforcement Learning
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropri...
Hamid R. Tizhoosh
ECML
2006
Springer
14 years 26 days ago
Reinforcement Learning for MDPs with Constraints
In this article, I will consider Markov Decision Processes with two criteria, each defined as the expected value of an infinite horizon cumulative return. The second criterion is e...
Peter Geibel
COLT
1994
Springer
14 years 3 months ago
Efficient Reinforcement Learning
Realistic domains for learning possess regularities that make it possible to generalize experience across related states. This paper explores an environment-modeling framework tha...
Claude-Nicolas Fiechter
AAAI
1998
14 years 8 days ago
A Framework for Reinforcement Learning on Real Robots
Learning on real robots in an real, unaltered environment provides an extremely challenging problem. Many of the simplifying assumptions made in other areas of learning cannot be ...
William D. Smart, Leslie Pack Kaelbling