Deep Gaussian Process Proximal Policy Optimization

TL;DR

This paper introduces GPPO, a scalable reinforcement learning algorithm that uses Deep Gaussian Processes to provide well-calibrated uncertainty estimates, enhancing safe exploration without sacrificing performance.

Contribution

The novel integration of Deep Gaussian Processes into a scalable actor-critic framework for RL, enabling uncertainty estimation in high-dimensional control tasks.

Findings

GPPO achieves competitive performance with PPO on benchmark tasks.

GPPO provides well-calibrated uncertainty estimates.

Enables safer and more effective exploration in RL.

Abstract

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.