Our recent paper “Reinforcement Learning with Unsupervised Auxiliary Tasks” introduces a method for greatly improving the learning speed and final performance of agents. We do this by augmenting the standard deep reinforcement learning methods with two main additional tasks for our agents to perform during training.

A visualisation of our agent in a Labyrinth maze foraging task can be seen below.