Classifying Options for Deep Reinforcement Learning
By Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath. 2016
22 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marta Garnelo
- Murray Shanahan
- Kai Arulkumaran
- Joelle Pineau
- J. Urgen Schmidhuber
- Sjoerd van Steenkiste
- Eric Crawford
- Hector Geffner
In Computer Networks and Communications
In Management Science and Operations Research
Citations in the corpus
- Towards Deep Symbolic Reinforcement Learning (2016, background): “Each of these shortcomings is an active area of research in the DRL community, where data efficient learning [8, 9], transfer learning [10, 11, 12, 13], planning [14, 15], and transparency [16] are all hot topics.”
- A Brief Survey of Deep Reinforcement Learning (2017, neutral): “Several works in DRL have attempted HRL by the use of one top-level policy that chooses between subpolicies, where the division of states or goals in to subpolicies is achieved either manually [1, 121, 54] or automatically [2, 129, 130].”