Reinforcement learning

We present a method for simplifying a learned reward model before visualizing it and show that this can make the reward more interpretable.

Reinforcement Learning consists of a few key building blocks that can be combined to create many of the well-known algorithms. Framing RL in terms of these building blocks can give a good overview and better understanding of these algorithms. This is the conclusion of a series with such an overview, covering model-based RL.

Reinforcement Learning consists of a few key building blocks that can be combined to create many of the well-known algorithms. Framing RL in terms of these building blocks can give a good overview and better understanding of these algorithms. This is part 2 of a series with such an overview, covering some policy optimization methods.

Reinforcement Learning consists of a few key building blocks that can be combined to create many of the well-known algorithms. Framing RL in terms of these building blocks can give a good overview and better understanding of these algorithms. This is part 1 of a series with such an overview, covering value-based methods (mainly in a tabular setting).