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Architecting and Visualizing Deep Reinforcement Learning Models

2021-12-02 17:48:26
Alexander Neuwirth, Derek Riley
arXiv_AI
       
Abstract

To meet the growing interest in Deep Reinforcement Learning (DRL), we sought to construct a DRL-driven Atari Pong agent and accompanying visualization tool. Existing approaches do not support the flexibility required to create an interactive exhibit with easily-configurable physics and a human-controlled player. Therefore, we constructed a new Pong game environment, discovered and addressed a number of unique data deficiencies that arise when applying DRL to a new environment, architected and tuned a policy gradient based DRL model, developed a real-time network visualization, and combined these elements into an interactive display to help build intuition and awareness of the mechanics of DRL inference.

Abstract (translated)

URL

https://arxiv.org/abs/2112.01451

PDF

https://arxiv.org/pdf/2112.01451.pdf

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