How to Choose a Reinforcement-Learning Algorithm
This work addresses the problem of algorithm selection for practitioners in reinforcement learning, but it is incremental as it organizes existing knowledge rather than introducing new methods.
The authors tackled the challenge of selecting reinforcement-learning algorithms by providing a structured overview and guidelines for choosing methods and action-distribution families, resulting in an interactive online tool available at https://rl-picker.github.io/.
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an algorithm for a task at hand can be challenging. In this work, we streamline the process of choosing reinforcement-learning algorithms and action-distribution families. We provide a structured overview of existing methods and their properties, as well as guidelines for when to choose which methods. An interactive version of these guidelines is available online at https://rl-picker.github.io/.