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AutoRL: AutoML for RL

Reinforcement learning (RL) has shown impressive results in a variety of applications. Well known examples include game and video game playing, robotics and, recently, “Autonomous navigation of stratospheric balloons”. A lot of the successes came about by combining the expressiveness of deep learning with the power of RL. Already on their own though, both frameworks […]

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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 […]

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