Speaker: Pierre Harvey Richemond, PhD student at the Data Science Institute with Imperial College After a successful career in quantitative finance, Pierre is researching deep learning and reinforcement learning at Data Science Institute. He holds several degrees in mathematics and engineering. Abstract: In this high-level talk, he will go through the latest recent and significant developments in the theory of reinforcement learning. Topics will range from soft Q-learning to proximal policy optimization and the Monte-Carlo tree search used in AlphaGo Zero. He will discuss strategies to implement these methods in Tensorflow, combine and replicate them in practice, and highlight connections with other related fields such as convex optimization and optimal transport. Thanks to all TensorFlow London meetup organisers and supporters: Seldon.io Altoros Rewired Google Developers Rise London