(12:56) Arthur recalled his decision to join Unity to work on its reinforcement learning problems.
(14:31) Arthur recalled his choice to do the Ph.D. part-time while working full-time.
(16:24) Arthur discussed problems with existing reinforcement learning simulation platforms and how the Unity Machine Learning Agents Toolkit addresses those.
(18:30) Arthur went over the challenges of maintaining and continuously iterating the Unity ML Agents toolkit.
(22:33) Arthur talked about the challenges of implementing such curiosity-based techniques.
(25:15) Arthur unpacked the introduction of the Obstacle Tower - a high fidelity, 3D, third person, procedurally generated environment - released in the latest version of the toolkit (read his blog post “On “solving” Montezuma’s Revenge”).
(29:15) Arthur discussed the Obstacle Tower Challenge, a contest that offers researchers and developers the chance to compete to train the best-performing agents on the Obstacle Tower Environment.
(32:49) Referring to his fun tutorial called “GANs explained with a classic sponge bob squarepants episode,” Arthur walked through the theory behind the Generative Adversarial Network algorithm via an explanation using an episode of Spongebob Squarepants.