# Gaussian Process Policy Optimization

arXiv: https://arxiv.org/abs/2003.01074 · HTML: https://mlsanity.com/paper/2003.01074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

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

- [Deep Gaussian Process Proximal Policy Optimization](https://mlsanity.com/paper/2511.18214/) (2025)

## Abstract (arXiv)

We propose a novel actor-critic, model-free reinforcement learning algorithm which employs a Bayesian method of parameter space exploration to solve environments. A Gaussian process is used to learn the expected return of a policy given the policy's parameters. The system is trained by updating the parameters using gradient descent on a new surrogate loss function consisting of the Proximal Policy Optimization 'Clipped' loss function and a bonus term representing the expected improvement acquisition function given by the Gaussian process. This new method is shown to be comparable to and at times empirically outperform current algorithms on environments that simulate robotic locomotion using the MuJoCo physics engine.