175 citations · 1.1k across the 47 of their papers we have counts for
9 papers · 1 filter
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
Colin Bellinger, Mark Crowley, Isaac Tamblyn
Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The…
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague +10
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by…
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems
Isaac Tamblyn, Tengkai Yu, Ian Benlolo
We discuss our simulation tool, fintech-kMC, which is designed to generate synthetic data for machine learning model development and testing. fintech-kMC is an agent-based model dr…
Training neural networks using Metropolis Monte Carlo and an adaptive variant
Stephen Whitelam, Viktor Selin, Ian Benlolo +2
We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical gro…
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
Colin Bellinger, Andriy Drozdyuk, Mark Crowley +1
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that me…
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are pres…
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Scientific intuition inspired by machine learning generated hypotheses
Pascal Friederich, Mario Krenn, Isaac Tamblyn +1
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…