# DAQN: Deep Auto-encoder and Q-Network

arXiv: https://arxiv.org/abs/1806.00630 · HTML: https://mlsanity.com/paper/1806.00630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://mlsanity.com/paper/2110.10963/) (2021)
- [Reinforcement Learning with External Knowledge by using Logical Neural Networks](https://mlsanity.com/paper/2103.02363/) (2021)
- [LOA: Logical Optimal Actions for Text-based Interaction Games](https://mlsanity.com/paper/2110.10973/) (2021)
- [BF++: a language for general-purpose program synthesis](https://mlsanity.com/paper/2101.09571/) (2021)
- [Self-evolving Autoencoder Embedded Q-Network](https://mlsanity.com/paper/2402.11604/) (2024)
- [Commonsense Knowledge from Scene Graphs for Textual Environments](https://mlsanity.com/paper/2210.14162/) (2022)

## Abstract (arXiv)

The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, and executing a lot of actions on the real robot also becomes a serious problem. Therefore, we propose an extended deep reinforcement learning method that is applied a generative model to initialize the network for reducing the number of training trials. In this paper, we used a deep q-network method as the deep reinforcement learning method and a deep auto-encoder as the generative model. We conducted experiments on three different tasks: a cart-pole game, an atari game, and a real-game with an actual robot. The proposed method trained efficiently on all tasks than the previous method, especially 2.5 times faster on a task with real environment images.