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» Iterative Learning Control - Monotonicity and Optimization
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ML
2002
ACM
100views Machine Learning» more  ML 2002»
14 years 9 months ago
Structure in the Space of Value Functions
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
David J. Foster, Peter Dayan
ICML
2006
IEEE
15 years 10 months ago
An analytic solution to discrete Bayesian reinforcement learning
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
ICMLA
2010
14 years 7 months ago
Multi-Agent Inverse Reinforcement Learning
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah,...
AMS
2005
Springer
112views Robotics» more  AMS 2005»
15 years 3 months ago
Combining Learning and Programming for High-Performance Robot Controllers
Abstract. The implementation of high-performance robot controllers for complex control tasks such as playing autonomous robot soccer is tedious, errorprone, and a never ending prog...
Alexandra Kirsch, Michael Beetz
IJCNN
2007
IEEE
15 years 4 months ago
Encoding Complete Body Models Enables Task Dependent Optimal Behavior
— Many neural network models of (human) motor learning focus on the acquisition of direct goal-to-action mappings, which results in rather inflexible motor control programs. We ...
Oliver Herbort, Martin V. Butz