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Article overview
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Interactive Visualization for Debugging RL | | Shuby Deshpande
; Benjamin Eysenbach
; Jeff Schneider
; | | Date: |
14 Aug 2020 | | Abstract: | Visualization tools for supervised learning allow users to interpret,
introspect, and gain an 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 as these tools
address challenges typically found in the supervised learning regime. In this
work, we design and implement an interactive visualization tool for debugging
and interpreting RL algorithms. Our system addresses many features missing from
previous tools such as (1) tools for supervised learning often are not
interactive; (2) while debugging RL policies researchers use state
representations that are different from those seen by the agent; (3) a
framework designed to make the debugging RL policies more conducive. We provide
an example workflow of how this system could be used, along with ideas for
future extensions. | | Source: | arXiv, 2008.07331 | | Services: | Forum | Review | PDF | Favorites | |
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