By In reinforcement learning (RL), one of the major machine learning (ML) paradigms, an agent interacts with an environment. How well an RL agent can solve a problem, can be sensitive to choices such as the policy network architecture, the training hyperparameters, or the specific dynamics of the environment. A common strategy to deal with […]
Automatic Reinforcement Learning for Molecular Design
Posted on August 7, 2019 by Frederic Runge, Danny Stoll