# Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1810.06339 · HTML: https://mlsanity.com/paper/1810.06339/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 14 of 14)

- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning](https://mlsanity.com/paper/2107.03961/) (2021)
- [PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.03970/) (2021)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Quantile-Based Policy Optimization for Reinforcement Learning](https://mlsanity.com/paper/2201.11463/) (2022)
- [Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks](https://mlsanity.com/paper/2201.06126/) (2022)
- [Policy Gradients for Probabilistic Constrained Reinforcement Learning](https://mlsanity.com/paper/2210.00596/) (2022)
- [Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints](https://mlsanity.com/paper/2205.15953/) (2022)
- [Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory](https://mlsanity.com/paper/2206.07483/) (2022)
- [ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator](https://mlsanity.com/paper/2109.11542/) (2021)
- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)
- [Reliable validation of Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2203.01075/) (2022)
- [RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers](https://mlsanity.com/paper/2205.13130/) (2022)
- [Learning to Play: Reinforcement Learning and Games](https://mlsanity.com/paper/2610.17391/) (2026)

## Abstract (arXiv)

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.