reinforcement learning, continuous domain, control
We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to problems where a priori action discretization is inadequate. The algorithm is an extension of a variable resolution technique that works for problems with continuous states and discrete actions. Results are given that indicate that JoSTLe is a promising step toward reinforcement learning in a fully continuous domain.
Original Publication Citation
Christopher K. Monson, David Wingate, Kevin D. Seppi, and Todd S. Peterson. "Variable Resolution Discretization in the Joint Space." In Proceedings of the International Conference on Machine Learning and Applications, Louisville, Kentucky, 24.
BYU ScholarsArchive Citation
Monson, Christopher K.; Seppi, Kevin; Wingate, David; and Peterson, Todd S., "Variable Resolution Discretization in the Joint Space" (2004). All Faculty Publications. 1036.
Physical and Mathematical Sciences
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