Modern Deep Reinforcement Learning Algorithms
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2021A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectivescited by 0
- 2022Weakly Supervised Scene Text Detection using Deep Reinforcement Learningcited by 0
- 2024A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimizationcited by 0
- 2024Model-free Distortion Canceling and Control of Quantum Devicescited by 0
- 2026Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farmscited by 0
- 2026Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms12 Jan 2026
- 2024Model-free Distortion Canceling and Control of Quantum Devices13 Jul 2024
- 2024A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization1 Jul 2024
- 2022Weakly Supervised Scene Text Detection using Deep Reinforcement Learning13 Jan 2022
- 2021A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectives4 Jan 2021