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» Reinforcement Learning for Operational Space Control
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132
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ICML
1994
IEEE
15 years 6 months ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
163
Voted
ABIALS
2008
Springer
15 years 9 months ago
A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion
Abstract. We present a model of motor learning based on a combination of Operational Space Control and Optimal Control. Anticipatory processes are used both in the learning of the ...
Camille Salaün, Vincent Padois, Olivier Sigau...
120
Voted
ECML
2006
Springer
15 years 6 months ago
Efficient Non-linear Control Through Neuroevolution
Abstract. Many complex control problems are not amenable to traditional controller design. Not only is it difficult to model real systems, but often it is unclear what kind of beha...
Faustino J. Gomez, Jürgen Schmidhuber, Risto ...
114
Voted
GECCO
2006
Springer
177views Optimization» more  GECCO 2006»
15 years 6 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
127
Voted
JMLR
2008
141views more  JMLR 2008»
15 years 2 months ago
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
Many complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but oft...
Faustino J. Gomez, Jürgen Schmidhuber, Risto ...