Bridging Reinforcement Learning and Creativity: Implementing Reinforcement Learning in Processing

Bridging Reinforcement Learning and Creativity: Implementing Reinforcement Learning in Processing

Abstract: Artists are underrepresented in the reinforcement learning (RL) community due to the steep learning curve involved in in-depth understanding of RL algorithms. However, artists can play an important role in the RL community by defining innovative problems, designing creative environments, and creating novel applications. As a popular tool for artists to experiment with programming, Processing has been highly adapted by many artists as their entry point to programming. Given the popularity of Processing in the creative community, we use this tutorial as a steppingstone to bridge RL and creativity by introducing RL core concepts in Processing. The purpose of this workshop is twofold: 1) to attract more artists to the RL community by demonstrating RL demos in their familiar IDE; 2) to demystify RL problems by implementing them in a high-level language without any external libraries. Importantly, this tutorial is not about introducing a specific programming language, but will focus on how to analyze, frame, and solve RL problems.

Authors/Presenter(s): Jieliang Luo, Media Arts & Technology; University of California, Santa Barbara, United States of America
Jieliang Luo, Media Arts & Technology; University of California, Santa Barbara, United States of America
Sam Green, Computer Science; University of California, Santa Barbara, United States of America