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SAB
2010
Springer
119views Optimization» more  SAB 2010»
13 years 3 months ago
Why and How Hippocampal Transition Cells Can Be Used in Reinforcement Learning
Julien Hirel, Philippe Gaussier, Mathias Quoy, Jea...
NIPS
2007
13 years 6 months ago
Hippocampal Contributions to Control: The Third Way
Recent experimental studies have focused on the specialization of different neural structures for different types of instrumental behavior. Recent theoretical work has provided no...
Máté Lengyel, Peter Dayan
AAAI
2008
13 years 7 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
JMLR
2010
148views more  JMLR 2010»
12 years 11 months ago
A Generalized Path Integral Control Approach to Reinforcement Learning
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
ATAL
2008
Springer
13 years 6 months ago
Sigma point policy iteration
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...