« Co-Explorers » are software agents that uses deep reinforcement learning to support interactive design space exploration.
They allow people to explore parametric design spaces by only communicating arbitrary preferences to the machine. A deep reinforcement learning algorithm is used to autonomously explore the space while interactively learning the user’s tastes—in a « co-exploration » workflow.
The current version of the software implements different user controls over agent’s exploration, along with a history that maps the space with user preferences. It is coded in Python; the OSC protocol supports connection between the deep reinforcement learning model (coded with the TensorFlow library) and parametric environments. Future developments may be released as a Python library and a Max/MSP object. It has been evaluated and used in workshops and practiced within the ægo artwork.
Available on GitHub