Deep Gaussian Process Proximal Policy Optimization
This addresses the need for safer and more effective exploration in reinforcement learning for control applications, but it is incremental as it builds on existing methods.
The paper tackled the problem of uncertainty estimation in reinforcement learning for control tasks by introducing Deep Gaussian Process Proximal Policy Optimization (GPPO), which achieved competitive performance with Proximal Policy Optimization on high-dimensional continuous control benchmarks while providing well-calibrated uncertainty estimates.
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.