Deep Gaussian Process Proximal Policy Optimization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningContinuous ControlGaussian ProcessesSimilar Papers 제목 키워드 기반
Riemannian Proximal Policy Optimization
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems. To model policy functions in MDP, we employ Gaussian mixture mod…
Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution
Reinforcement learning methods for continuous control tasks have evolved in recent years generating a family of policy gradient methods that rely primarily on a Gaussian distribution for modeling a stochastic policy. How…
continuous-controlContinuous ControlOpenAI GymPolicy Gradient MethodsGaussian Process Policy Optimization
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 o…
MuJoCoreinforcement-learningReinforcement LearningReinforcement Learning (RL)Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Methods such as Flow-GRPO and CPS cast the …
Reinforcement LearningVideo GenerationPath-Space Mirror Descent for On-Policy Reinforcement Learning under the Generalized Schrödinger Bridge
Classical on-policy algorithms such as PPO and mirror descent policy optimization provide stable proximal policy updates through tractable action likelihoods, but are typically instantiated with simple Gaussian policies …
Reinforcement Learning