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IJCNN
2006
IEEE
14 years 1 months ago
Reinforcement Learning for Parameterized Motor Primitives
Abstract— One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the “building blocks of movement genera...
Jan Peters, Stefan Schaal
SIAMCO
2000
117views more  SIAMCO 2000»
13 years 7 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
JMLR
2002
125views more  JMLR 2002»
13 years 7 months ago
Lyapunov Design for Safe Reinforcement Learning
Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system'...
Theodore J. Perkins, Andrew G. Barto
ESANN
2006
13 years 9 months ago
Construction of a memory management system in an on-line learning mechanism
This paper is the first of a two paper series that deals with an important problem in on-line learning mechanisms for autonomous agents that must perform non trivial tasks and oper...
Francisco Bellas, José Antonio Becerra, Ric...
ICML
2005
IEEE
14 years 8 months ago
Reinforcement learning with Gaussian processes
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Yaakov Engel, Shie Mannor, Ron Meir