Tom Schaul

Reinforcement Learning with Unsupervised Auxiliary Tasks

2016 · arXiv (Cornell University)

Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many …