ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry

no code implementations • 23 May 2023 • Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn

This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery.

reinforcement-learning Reinforcement Learning (RL)

Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment

no code implementations • 29 Dec 2021 • Chris Beeler, Xinkai Li, Colin Bellinger, Mark Crowley, Maia Fraser, Isaac Tamblyn

Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process (POMDP) is known.

Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning

1 code implementation • 20 Mar 2019 • Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn

Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle.

reinforcement-learning Reinforcement Learning (RL)

Extensive deep neural networks for transferring small scale learning to large scale systems

no code implementations • 17 Aug 2017 • Kyle Mills, Kevin Ryczko, Iryna Luchak, Adam Domurad, Chris Beeler, Isaac Tamblyn

We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets.

Computational Physics Materials Science