Implicitly Quantized Representations for Reinforcement Learning
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent …
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent …
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 …
Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.
In this talk I will present our ICRA 2021 paper "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation".
Minimal Gaussian process library in JAX with a simple (custom) approach to state management.
Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.
This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds …
This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are …