4 citations · 4 across the 2 of their papers we have counts for
4 papers
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…
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Chris Beeler, Xinkai Li, Colin Bellinger +3
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…
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
Chris Beeler, Uladzimir Yahorau, Rory Coles +3
Using a model heat engine, we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We consider both gradient and gra…
Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills, Kevin Ryczko, Iryna Luchak +3
We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily…