Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the generation of high dimensional potential models for a wide variety of materials.
- Sukriti Manna
- Troy D. Loeffler
- Subramanian K. R. S. Sankaranarayanan
