Random Sampling of States in Dynamic Programming
IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics (2008)
- ISSN: 10834419
- DOI: 10.1109/TSMCB.2008.926610
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Abstract
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on finding steady-state policies for deterministic time-invariant discrete time control problems with continuous states and actions often found in robotics. In this paper, we describe our approach and provide initial results on several simulated robotics problems.
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Random Sampling of States in Dynamic Programming
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