Timothy Lillicrap

Wednesday 1st November 2017

Time: 2.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

Deep reinforcement learning: Recent advances and connections with the brain

There has been rapid progress in the application of reinforcement learning to difficult problems such as playing video games from raw-pixels, controlling high-dimensional motor systems, and winning at the games of Go and Poker. These recent advances in reinforcement learning have been built on top of deep neural network function approximators and the backpropagation of error algorithm. Large networks are key to success, and to train these networks effectively reinforcement algorithms typically backpropogate either TD-errors (e.g. DQN) or policy gradients (e.g. TRPO and A3C) or both (e.g. DDPG). Whether the brain employs deep learning algorithms remains contentious, and just how the brain might implement approximations of the backprop algorithm remains a mystery. I will review recent progress in deep reinforcement learning and argue that these results further compel us to investigate whether the brain implements some form of deep learning.