Modern Deep Reinforcement Learning Algorithms
2019 Β· Sergey Ivanov, Alexander D'Yakonov
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.
Authors
(none)
Tags
Stats
Related papers
- A Comprehensive Survey Of Reinforcement Learning: From Algorithms To Practical Challenges (2024)0.00
- A Survey Of Deep Reinforcement Learning In Video Games (2019)0.00
- Distributed Deep Reinforcement Learning: An Overview (2020)0.00
- On The Mistaken Assumption Of Interchangeable Deep Reinforcement Learning Implementations (2025)0.00
- Dopamine: A Research Framework For Deep Reinforcement Learning (2018)0.00
- Deep Reinforcement Learning For Multi-agent Systems: A Review Of Challenges, Solutions And Applications (2018)22.57
- Evaluating The Progress Of Deep Reinforcement Learning In The Real World: Aligning Domain-agnostic And Domain-specific Research (2021)0.00
- A Survey On Deep Reinforcement Learning-based Approaches For Adaptation And Generalization (2022)0.00