Scientists from Google AI have published exciting research regarding unsupervised skill discovery in deep reinforcement learning. Essentially it will be possible to utilize unsupervised learning methods to learn model dynamics and promising skills in an unsupervised, model-free reinforcement learning enviroment, subsequently enabling to use model-based planning methods in model-free reinforcement learning setups.
Many deep reinforcement learning methods have been established for the development of autonomous AI-agents. This talk introduces deep reinforcement learning as combination of deep learning and reinforcement learning and highlights a selection of noteworthy advancements since Mnih et al. introduced Deep Q-learning.