175 citations · 1.1k across the 47 of their papers we have counts for
4 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…
Automatic graph representation algorithm for heterogeneous catalysis
Zachary Gariepy, ZhiWen Chen, Isaac Tamblyn +2
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, m…
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…