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BWL XI: Software Paper on Reinforcement Learning in Journal of Open Source Software

A new software paper has been accepted for publication in Journal of Open Source Software. The paper presents the first R package for performing model-free reinforcement learning in R.


Title: ReinforcementLearning: A Package to Perform Model-Free Reinforcement Learning in R

Authors: Nicolas Pröllochs, Stefan Feuerriegel (ETH Zurich)

 

Abstract:

Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error. It differs from supervised learning, since reinforcement learning requires no explicit labels; instead, the agent interacts continuously with its environment. That is, the agent starts in a specific state and then performs an action, based on which it transitions to a new state and, depending on the outcome, receives a reward. Different strategies (e.g. Q-learning) have been proposed to maximize the overall reward, resulting in a so-called policy, which defines the best possible action in each state. Mathematically, this process can be formalized by a Markov decision process and it has been implemented by packages in R; however, there is currently no package available for reinforcement learning. As a remedy, this paper demonstrates how to perform reinforcement learning in R and, for this purpose, introduces the ReinforcementLearning package. The package provides a remarkably flexible framework and is easily applied to a wide range of different problems. We demonstrate its use by drawing upon common examples from the literature (e.g. finding optimal game strategies).

 

The ReinforcementLearning R package is available on CRAN.