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» Stable Function Approximation in Dynamic Programming
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ICML
2006
IEEE
13 years 11 months ago
Automatic basis function construction for approximate dynamic programming and reinforcement learning
We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results ...
Philipp W. Keller, Shie Mannor, Doina Precup
TSMC
1998
135views more  TSMC 1998»
13 years 5 months ago
Universal stabilization using control Lyapunov functions, adaptive derivative feedback, and neural network approximators
— In this paper, the problem of stabilization of unknown nonlinear dynamical systems is considered. An adaptive feedback law is constructed that is based on the switching adaptiv...
Elias B. Kosmatopoulos
ICRA
2002
IEEE
147views Robotics» more  ICRA 2002»
13 years 10 months ago
Design of Asymptotically Stable Walking for a 5-Link Planar Biped Walker via Optimization
— Closed-loop, asymptotically stable walking motions are designed for a 5-link, planar bipedal robot model with one degree of underactuation. Parameter optimization is applied to...
E. R. Westervelt, J. W. Grizzle
CORR
2010
Springer
119views Education» more  CORR 2010»
13 years 5 months ago
Dynamic Policy Programming
In this paper, we consider the problem of planning and learning in the infinite-horizon discounted-reward Markov decision problems. We propose a novel iterative direct policysearc...
Mohammad Gheshlaghi Azar, Hilbert J. Kappen
NIPS
1994
13 years 6 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore