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Discrete Codebook World Models for Continuous Control

In reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future …

Aidan Scannell, Mohammadreza Nakhaei, Kalle Kujanpää, Yi Zhao, Kevin Luck, Arno Solin, Joni Pajarinen

Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning

Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to new tasks by training on data from a set …

Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen

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

Offline reinforcement learning (RL) allows learning sequential behavior from fixed datasets. Since offline datasets do not cover all …

Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen

Function-space Parameterization of Neural Networks for Sequential Learning

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining …

Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

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 variable associated with the environment’s dynamics. As such, it is 1) unknown a priori and 2) we do not observe its output from the environment, so cannot learn it with supervised learning.

Aidan Scannell, Carl Henrik Ek, Arthur Richards

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

Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.

Aidan Scannell, Carl Henrik Ek, Arthur Richards

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