mlsanityData to 14 Oct 2026

Deep Gaussian Process Proximal Policy Optimization

arXiv 2511.18214 ↗First posted 22 Nov 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in RL, they often lack calibrated uncertainty estimates. We introduce Deep Gaussian Process Proximal Policy Optimization (GPPO), a scalable, model-free actor-critic algorithm that leverages Deep Gaussian Processes (DGPs) to approximate both the policy and value function. GPPO maintains competitive performance with respect to Proximal Policy Optimization on standard high-dimensional continuous control benchmarks while providing well-calibrated uncertainty estimates that can inform safer and more effective exploration.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 23 references that are in the map. First posted on arXiv 22 Nov 2025.

Builds on

11 of 23 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  4. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  5. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  6. 2017Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Controlcited by 53
  7. 2022A Review of Uncertainty for Deep Reinforcement Learningcited by 40
  8. 2022Mirror Learning: A Unifying Framework of Policy Optimisationcited by 31
  9. 2020Deep Sigma Point Processescited by 17
  10. 2020Gaussian Process Policy Optimizationcited by 1
  11. 2025Evaluating Uncertainty in Deep Gaussian Processescited by 1

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  1. 2025Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
  2. 2025TIC-GRPO: Provable and Efficient Optimization for Reinforcement Learning from Human Feedback
  3. 2026GIPO: Gaussian Importance Sampling Policy Optimization
  4. 2024Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
  5. 2026expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling
  6. 2026ExO-PPO: an Extended Off-policy Proximal Policy Optimization Algorithm
  7. 2025A Practical Introduction to Deep Reinforcement Learning
  8. 2026ERPPO: Entropy Regularization-based Proximal Policy Optimization