Gaussian-Processes

Function-space Parameterization of Neural Networks for Sequential Learning

Function-Space Bayesian Deep Learning for Sequential Learning

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes …

Sparse Function-space Representation of Neural Networks

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden …

Model-based reinforcement learning under uncertainty

Investigating Bayesian Neural Network Dynamics Models for Model-Based Reinforcement Learning

This project seeks to evaluate and compare different approaches for learning dynamics models in model-based RL. In particular, we plan to compare different approximate inference …

Mode-Constrained Exploration for Model-Based Reinforcement Learning

This work presents a learning-based control method for navigating to a target state in unknown, or partially unknown, multimodal dynamical systems. In particular, it develops a …

PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems

Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

In this talk I will present our ICRA 2021 paper "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation".

Identifiable Mixtures of Sparse Variational Gaussian Process Experts

Aidan Scannell

• 26 May, 2021 • 1 min read

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