Tabula Rasa is a data-driven video installation. Inspired by the philosophical concept of the clean slate, it explores the technology of Deep Reinforcement Learning in machine learning.
An agent’s training is visually interpreted through abstract-organic aesthetics as it is simulated in a fluid environment. As the agent’s single goal is to move forward, all motion data has been generated in its unsupervised learning process.
Concept
According to the theory of Tabula rasa, the agent learns by constantly interacting with the environment. Like on a blank sheet of paper, it starts acting without any experience. The only motivation to progress and learn is to be rewarded as high as possible.
Generative Approach
To visualize this learning progress, the agent’s appearance corresponds to the training’s state at a time. The rewards and the strategy performance determine the colors and look of the geometry.
Visuality
The visible progress and the interval until the training’s completion are visualized distinctively. To demonstrate differences in unsupervised learning processes, multiple agents, spawn parallel, not interacting with each other.
Music
Each layer of the accompanying music is metonymy and a metaphor for life events. It overlaps, shakes, disappears, accumulates, shifts between simple and complicated. This process is irreversible: As the agent learns to move forward, time and sound move forward.
Created at Berlin University of the Arts
Class: Methodenlabor
In Collaboration with: Zihern Lee, Adrian Pfisterer and Niklas Thran
Supervision: Bernd Grether

Special Thanks to:

Christian Block and Funkhaus Berlin
FSR Medienhaus UdK Berlin
HfG Schwäbisch Gmünd
Torsten Dodillet, Technischer Lehrer für Fotografie
Dipl.-Des. (FH) Rasih Bayölken
Prof. Marc Guntow and Prof. Ralf Dringenberg
Nick Morgan-Jones

Models: Georgianna Manafa and Ohad Ben Moshe

Other works

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