Volodymyr Mnih

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 …

Discovering Diverse Nearly Optimal Policies withSuccessor Features.

2021 · arXiv (Cornell University)

Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, …