# Distributed Deep Reinforcement Learning: An Overview

arXiv: https://arxiv.org/abs/2011.11012 · HTML: https://mlsanity.com/paper/2011.11012/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey](https://mlsanity.com/paper/2411.05614/) (2024)
- [Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox](https://mlsanity.com/paper/2212.00253/) (2022)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [AI in Human-computer Gaming: Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2111.07631/) (2021)
- [Group-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.05135/) (2022)
- [Private Knowledge Sharing in Distributed Learning: A Survey](https://mlsanity.com/paper/2402.06682/) (2024)
- [Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading](https://mlsanity.com/paper/2509.09176/) (2025)
- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)
- [Efficient Parallel Reinforcement Learning Framework using the Reactor Model](https://mlsanity.com/paper/2312.04704/) (2023)

## Abstract (arXiv)

Deep reinforcement learning (DRL) is a very active research area. However, several technical and scientific issues require to be addressed, amongst which we can mention data inefficiency, exploration-exploitation trade-off, and multi-task learning. Therefore, distributed modifications of DRL were introduced; agents that could be run on many machines simultaneously. In this article, we provide a survey of the role of the distributed approaches in DRL. We overview the state of the field, by studying the key research works that have a significant impact on how we can use distributed methods in DRL. We choose to overview these papers, from the perspective of distributed learning, and not the aspect of innovations in reinforcement learning algorithms. Also, we evaluate these methods on different tasks and compare their performance with each other and with single actor and learner agents.