DocumentCode
2717455
Title
Randomly Sampling Actions In Dynamic Programming
Author
Atkeson, Christopher G.
Author_Institution
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA
fYear
2007
fDate
1-5 April 2007
Firstpage
185
Lastpage
192
Abstract
We describe an approach towards reducing the curse of dimensionality for deterministic dynamic programming with continuous actions by randomly sampling actions while computing a steady state value function and policy. This approach results in globally optimized actions, without searching over a discretized multidimensional grid. We present results on finding time invariant control laws for two, four, and six dimensional deterministic swing up problems with up to 480 million discretized states
Keywords
dynamic programming; random processes; sampling methods; deterministic dynamic programming; randomly sampling; time invariant control laws; value function; Computational efficiency; Cost function; Dynamic programming; Interpolation; Learning; Multidimensional systems; Robots; Sampling methods; Steady-state; USA Councils;
fLanguage
English
Publisher
ieee
Conference_Titel
Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0706-0
Type
conf
DOI
10.1109/ADPRL.2007.368187
Filename
4220832
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