Play the Reinforcement Learning Agent

Helene Plisnier, Alessandro Antonio Fasano, Ann Nowe

Onderzoeksoutput: Unpublished paper

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In the past few decades, artificial intelligence has gained an increasing amount of interest from the general public. Accompanying this interest, comes expectations of how sophisticated AI methods and their abilities are, often without a proper understanding of how they actually work. This demonstration is meant to give non-expert participants an idea of the view an RL agent has of its environment. We invite a volunteer to take the place of a standard RL agent and try learning the task solely based on information that would be available in a typical RL setting. The purpose of this demonstration is to illustrate how unintuitive an RL agent's perspective of its environments is from a human point of view, and hence how limited its understanding of the task it is learning is. By establishing this idea in non-experts minds, we hope to debunk certain inaccurate assumptions people may have about AI technologies, specifically RL in this case.


Conference33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Verkorte titelBNAIC/BeneLearn 2021
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