# Modern Deep Reinforcement Learning Algorithms

arXiv: https://arxiv.org/abs/1906.10025 · HTML: https://mlsanity.com/paper/1906.10025/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectives](https://mlsanity.com/paper/2101.00829/) (2021)
- [Weakly Supervised Scene Text Detection using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.04866/) (2022)
- [A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization](https://mlsanity.com/paper/2407.01653/) (2024)
- [Model-free Distortion Canceling and Control of Quantum Devices](https://mlsanity.com/paper/2407.09877/) (2024)
- [Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms](https://mlsanity.com/paper/2601.08052/) (2026)

## Abstract (arXiv)

Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research. In this work latest DRL algorithms are reviewed with a focus on their theoretical justification, practical limitations and observed empirical properties.