Computational Creativity with Reinforcement Learning and/or Generative Adversarial Networks

Read also: Writing a Master's Thesis in Computational Creativity

There are three basic machine learning paradigms: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning requires data instances annotated with the correct output labels, but the other paradigms assume no such labelled data. Reinforcement learning (RL) is primarily concerned with what actions an intelligent agent (or group of agents) should take in a specified environment in order to maximise some cumulative reward, while simultaneously exploring the environment and exploiting previously accumulated knowledge.

Within computational creativity applications, reinforcement learning from human feedback has been utilised for language generation in systems such as ChatGPT but can tentatively have a wider usage in art or music, or can be used form part of a generative adversarial network (GAN) that so far mainly have been explored in image generation.