# Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference

arXiv: https://arxiv.org/abs/1709.04221 · HTML: https://mlsanity.com/paper/1709.04221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Composable Learning with Sparse Kernel Representations](https://mlsanity.com/paper/2103.14474/) (2021)

## Abstract (arXiv)

We consider policy evaluation in infinite-horizon discounted Markov decision problems (MDPs) with infinite spaces. We reformulate this task a compositional stochastic program with a function-valued decision variable that belongs to a reproducing kernel Hilbert space (RKHS). We approach this problem via a new functional generalization of stochastic quasi-gradient methods operating in tandem with stochastic sparse subspace projections. The result is an extension of gradient temporal difference learning that yields nonlinearly parameterized value function estimates of the solution to the Bellman evaluation equation. Our main contribution is a memory-efficient non-parametric stochastic method guaranteed to converge exactly to the Bellman fixed point with probability 1 with attenuating step-sizes. Further, with constant step-sizes, we obtain mean convergence to a neighborhood and that the value function estimates have finite complexity. In the Mountain Car domain, we observe faster convergence to lower Bellman error solutions than existing approaches with a fraction of the required memory.