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cs.LG2022★ 1 cited
Investigating the Properties of Neural Network Representations in Reinforcement Learning
Han Wang, Erfan Miahi, Martha White +5
In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning…
cs.LG2022★ 2 cited
Continual Auxiliary Task Learning
Matthew McLeod, Chunlok Lo, Matthew Schlegel +4
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms hav…