TY - UNPB AB - Deep reinforcement learning is the learning of multiple levels ofhierarchical representations for reinforcement learning. Hierarchicalreinforcement learning focuses on temporal abstractions in planning andlearning, allowing temporally-extended actions to be transferred between tasks.In this paper we combine one method for hierarchical reinforcement learning -the options framework - with deep Q-networks (DQNs) through the use ofdifferent "option heads" on the policy network, and a supervisory network forchoosing between the different options. We show that in a domain where we haveprior knowledge of the mapping between states and options, our augmented DQNachieves a policy competitive with that of a standard DQN, but with much lowersample complexity. This is achieved through a straightforward architecturaladjustment to the DQN, as well as an additional supervised neural network. AU - Arulkumaran,K AU - Dilokthanakul,N AU - Shanahan,M AU - Bharath,AA PB - IJCAI PY - 2016/// TI - Classifying options for deep reinforcement learning UR - http://arxiv.org/abs/1604.08153v1 UR - http://hdl.handle.net/10044/1/32327 ER -