Automatic Reinforcement Learning for Molecular Design

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 […]