# Faster Deep Q-learning using Neural Episodic Control

arXiv: https://arxiv.org/abs/1801.01968 · HTML: https://mlsanity.com/paper/1801.01968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [Model-Based Episodic Memory Induces Dynamic Hybrid Controls](https://mlsanity.com/paper/2111.02104/) (2021)
- [Model-Free Deep Reinforcement Learning in Software-Defined Networks](https://mlsanity.com/paper/2209.01490/) (2022)
- [Incentivising cooperation by rewarding the weakest member](https://mlsanity.com/paper/2212.00119/) (2022)
- [Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks](https://mlsanity.com/paper/2308.04909/) (2023)

## Abstract (arXiv)

The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment. We propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN by using good one such as NEC at the beginning of learning. We show it is able to learn faster than Double DQN or N-step DQN in the experiments of Pong.