{G}aussian processes in reinforcement learning

  • Rasmussen C
  • Kuß M
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We exploit some useful properties of Gaussian process (GP) regression
models for reinforcement learning in continuous state spaces and
discrete time. We demonstrate how the GP model allows evaluation
of the value function in closed form. The resulting policy iteration
algorithm is demonstrated on a simple problem with a two dimensional
state space. Further, we speculate that the intrinsic ability of
GP models to characterise distributions of functions would allow
the method to capture entire distributions over future values instead
of merely their expectation, which has traditionally been the focus
of much of reinforcement learning.

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  • Carl Edward Rasmussen

  • Malte Kuß

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