# Classifying Options for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1604.08153 · HTML: https://mlsanity.com/paper/1604.08153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 1 of 1)

- [Simultaneously Updating All Persistence Values in Reinforcement Learning](https://mlsanity.com/paper/2211.11620/) (2022)

## Abstract (arXiv)

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.