mlsanityData to 14 Oct 2026

Policy Gradient with Kernel Quadrature

arXiv 2310.14768 ↗First posted 23 Oct 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement learning tasks. Our aim in this paper is to select a small but representative subset of a large batch of episodes, only on which we actually compute rewards for more efficient policy gradient iterations. We build a Gaussian process modeling of discounted returns or rewards to derive a positive definite kernel on the space of episodes, run an ``episodic" kernel quadrature method to compress the information of sample episodes, and pass the reduced episodes to the policy network for gradient updates. We present the theoretical background of this procedure as well as its numerical illustrations in MuJoCo tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 59 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

27 of 59 references.

References · 27

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15 more
  1. 2021Generalized Kernel Thinningcited by 29
  2. 2019Kernel quadrature with DPPscited by 27
  3. 2021Positively Weighted Kernel Quadrature via Subsamplingcited by 25
  4. 2016Monte Carlo with Determinantal Point Processescited by 22
  5. 2020Kernel interpolation with continuous volume samplingcited by 21
  6. 2018Active Mini-Batch Sampling using Repulsive Point Processescited by 20
  7. 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
  8. 2021Distribution Compression in Near-linear Timecited by 19
  9. 2023Sampling-based Nystr"om Approximation and Kernel Quadraturecited by 17
  10. 2021Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGDcited by 12
  11. 2023Kernel quadrature with randomly pivoted Choleskycited by 11
  12. 2017On the Sampling Problem for Kernel Quadraturecited by 7
  13. 2022Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learningcited by 7
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  15. 2020Deep Bayesian Quadrature Policy Optimizationcited by 4

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