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29 March 2024
 
  » arxiv » 2007.5577

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Vizarel: A System to Help Better Understand RL Agents
Shuby Deshpande ; Jeff Schneider ;
Date 10 Jul 2020
AbstractVisualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initial attempt at constructing a prototype of these ideas, through identifying possible features that such a system should encapsulate. Our design is motivated by envisioning the system to be a platform on which to experiment with interpretable reinforcement learning.
Source arXiv, 2007.5577
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