Deep reinforcement learning: a survey

By Haonan Wang, Ning Liu, Yiyun Zhang, Dawei Feng, Feng Huang, Dongsheng Li, Yiming Zhang. Frontiers of Information Technology & Electronic Engineering, 2020

780 citations recorded by the source corpus.

Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods, model-free methods, and advanced RL methods. We thoroughly analyze the advances including exploration, inverse RL, and transfer RL. Finally, we outline the current representative applications, and analyze four open problems for future research.

Subfields: Artificial Intelligence, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/16e83f3f0f78ceb203746eeb88f1f5aae9ba3092

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In Artificial Intelligence

1. Esther Luna Colombini
2. Rafael Figueiredo Prudencio
3. Marcos Ricardo Omena de Albuquerque Máximo

Citations in the corpus

• A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems (2022, background): “These quantities are used throughout the RL field, which is conventionally subdivided into three classes of methods: dynamic programming, model free, and model based [31], [32].”