Deep Reinforcement Learning with Double Q-learning

Published November 1, 2023


1 min read

Image of Jarek Liesen

Jarek Liesen

“Deep Reinforcement Learning with Double Q-learning” by Hado van Hasselt, Arthur Guez, and David Silver, published in 2016, presents a significant breakthrough in reinforcement learning. The paper addresses the issue of action value overestimation in Q-learning and introduces a novel solution. It highlights the prevalence of overestimations in practice and their negative impact on performance. By extending the concept of Double Q-learning to large-scale function approximation and adapting it to the DQN algorithm, the authors effectively reduce overestimations, leading to substantial performance improvements in various games. This work not only identifies a key limitation in Q-learning but also demonstrates the potential for innovative algorithms to enhance reinforcement learning outcomes.

Link to the paper: