1 code implementation • 28 Sep 2022 • Edward Gillman, Dominic C. Rose, Juan P. Garrahan
We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks.
no code implementations • 22 Sep 2022 • Jamie F. Mair, Dominic C. Rose, Juan P. Garrahan
In machine learning, there is renewed interest in neural network ensembles (NNEs), whereby predictions are obtained as an aggregate from a diverse set of smaller models, rather than from a single larger model.
no code implementations • 10 May 2021 • Avishek Das, Dominic C. Rose, Juan P. Garrahan, David T. Limmer
We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning.
1 code implementation • 26 May 2020 • Dominic C. Rose, Jamie F. Mair, Juan P. Garrahan
By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest.
no code implementations • 12 Feb 2020 • Edward Gillman, Dominic C. Rose, Juan P. Garrahan
Tensor network (TN) techniques - often used in the context of quantum many-body physics - have shown promise as a tool for tackling machine learning (ML) problems.