EndtoEnd Learning of Agents
Reinforcement learning agents have achieved some successes in a variety of domains, however their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, lowdimensional state spaces. In this talk I will explain a novel algorithm (Deep QNetwork) that combines deep learning and reinforcement learning to enable agents to derive efficient representations of the environment from highdimensional sensory inputs, and use these to generalize past experience to new situations. The Deep QNetwork (DQN) algorithm achieves human level performance on ATARI 2600 domain operating directly on raw images and game scores.
Koray Kavukcuoglu, PhD, Principal Researcher. Trained and worked as an aerospace engineer, before doing a machine learning PhD at NYU with Yann LeCun. Whilst there, he co-wrote the Torch platform, one of the most heavily used machine learning libraries in the world. Following his PhD, Koray was a Senior Researcher at Princeton/NEC labs, where he worked on applying cutting edge ML techniques