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» Compositional Models for Reinforcement Learning
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AAAI
2008
13 years 10 months ago
Maximum Entropy Inverse Reinforcement Learning
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recoveri...
Brian Ziebart, Andrew L. Maas, J. Andrew Bagnell, ...
AAAI
2008
13 years 10 months ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
ABIALS
2008
Springer
13 years 9 months ago
Multiscale Anticipatory Behavior by Hierarchical Reinforcement Learning
Abstract. In order to establish autonomous behavior for technical systems, the well known trade-off between reactive control and deliberative planning has to be considered. Within ...
Matthias Rungger, Hao Ding, Olaf Stursberg
ITNG
2007
IEEE
14 years 1 months ago
Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals
This paper presents the recognition of Handwritten Hindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance f...
Madasu Hanmandlu, J. Grover, Vamsi Krishna Madasu,...
ATAL
2003
Springer
14 years 28 days ago
A selection-mutation model for q-learning in multi-agent systems
Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The fe...
Karl Tuyls, Katja Verbeeck, Tom Lenaerts