Classifying Options for Deep Reinforcement Learning

June, 2016


In this paper we combine one method for hierarchical reinforcement
learning - the options framework - with deep Q-networks (DQNs) through
the use of different "option heads" on the policy network, and a
supervisory network for choosing between the different options. We
utilise our setup to investigate the effects of architectural
constraints in subtasks with positive and negative transfer, across a
range of network capacities. We empirically show that our augmented
DQN has lower sample complexity when simultaneously learning subtasks
with negative transfer, without degrading performance when learning
subtasks with positive transfer.

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