Modern Deep Reinforcement Learning Algorithms

Sergey Ivanov, Alexander D'yakonov
6/24/2019
cs.LGcs.AIstat.ML

Official code: FortsAndMills/Learning-Reinforcement-Learning (29 stars, last commit 2019-11-06).

Abstract

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.

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FortsAndMills/Learning-Reinforcement-LearningOfficial100%
294Python, Jupyter NotebookAug 25, 20186 years ago

Cite this paper

@article{ivanov2019modern,
  title  = {Modern Deep Reinforcement Learning Algorithms},
  author = {Sergey Ivanov and Alexander D'yakonov},
  year   = {2019},
  eprint = {1906.10025},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1906.10025}
}

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