Mathematics of Deep Learning
Spring 2020

Deep value-based RL and DQN


In the introduction, we saw value-based RL algorithms (and specifically Q learning) in the tabular setting where we keep a separate Q value for each $ s,a$ pair. If we want to scale to large state spaces we will need to be able to generalize across an infinite state space using a function approximator, like a neural network. This week we will see how Q-learning can be modified to support function approximation and read the influential paper from Deepmind introducing the deep Q network (DQN) algorithm.


Required reading

Optional reading