Reinforcement-Learning

Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning

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 …

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 …

Neural Networks as Sparse Gaussian Processes for Sequential Learning

I will be presenting our research on bayesian deep learning for sequential learning at the [International Workshop of Intelligent Autonomous Learning Systems …

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 …

Model-based reinforcement learning under uncertainty: the importance of knowing what you don't know

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.

Aalto RL Reading Club: Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies with One Objective