Architecting and Visualizing Deep Reinforcement Learning Models

• URL: http://arxiv.org/abs/2112.01451v1
• Date: Thu, 2 Dec 2021 17:48:26 GMT
• Title: Architecting and Visualizing Deep Reinforcement Learning Models
• Authors: Alexander Neuwirth and Derek Riley
• Abstract summary: Deep Reinforcement Learning (DRL) is a theory that aims to teach computers how to communicate with each other. In this paper, we present a new Atari Pong game environment, a policy gradient based DRL model, a real-time network visualization, and an interactive display to help build intuition and awareness of the mechanics of DRL inference.
• Score: 77.34726150561087
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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