University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Reinforcement Learning using Policy Gradients in Reproducing Kernel Hilbert Space

Reinforcement Learning using Policy Gradients in Reproducing Kernel Hilbert Space

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If you have a question about this talk, please contact Lars Kunze.

Host: Dr. Ata Kaban

I will present a system for non-parametric policy search in reproducing kernel Hilbert space for solving reinforcement learning problems. The method has many benefits over standard parametric approaches: policies can be modeled in rich function classes; there is less need to rescale the search space using, e.g., natural gradients; the policy gradient can be easily derived and estimated; the method is adaptive to the complexity of the problem. The system uses sparse-greedy approaches to function estimation both to estimate the value function, and to maintain a compact, but expressive, policy representation. I will demonstrate the method on benchmark MDPs and simulated quadrocopter navigation experiments. If time permits I will present recent extensions to second order policy search methods.

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This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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