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Thursday, July 6 • 1:30pm - 1:48pm
ReinforcementLearning: A package for replicating human behavior in R

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Keywords: Reinforcement Learning, Human-Like Learning, Experience Replay, Q-Learning, Decision Analytics
Webpages: https://github.com/nproellochs/ReinforcementLearning
Reinforcement learning has recently gained a great deal of traction in studies that call for human-like learning. In settings where an explicit teacher is not available, this method teaches an agent via interaction with its environment without any supervision other than its own decision-making policy. In many cases, this approach appears quite natural by mimicking the fundamental way humans learn. However, implementing reinforcement learning is programmatically challenging, since it relies on continuous interactions between an agent and its environment. In fact, there is currently no package available that performs model-free reinforcement learning in R. As a remedy, we introduce the ReinforcementLearning R package, which allows an agent to learn optimal behavior based on sample experience consisting of states, actions and rewards. The result of the learning process is a highly interpretable reinforcement learning policy that defines the best possible action in each state. The package provides a remarkably flexible framework and is easily applied to a wide range of different problems. We demonstrate the added benefits of human-like learning using multiple real-world examples, e.g. by teaching the optimal movements of a robot in a grid map.

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Thursday July 6, 2017 1:30pm - 1:48pm CEST
3.02 Wild Gallery