Results 11 to 20 of about 8,331,707 (294)
Explainability in deep reinforcement learning [PDF]
Knowledge-Based Systems, 2021 Article accepted at Knowledge-Based ...
Alexandre Heuillet +2 more
openaire +3 more sources
Reinforcement Learning-based Spectrum Sharing for Cognitive Radio [PDF]
, 2011 This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system.
Jiang, Tao
core +6 more sources
Job-Scheduling-Deep-Reinforcement-Learning
, 2023 A novel approach to Job Scheduling applying Deep Reinforcement Learning and Neural Network Architectures of Natural Language ProcessingIf you use this software, please cite it as ...
de Oliveira Hitzges, Diego
core +2 more sources
A survey on deep reinforcement learning for audio‑based applications [PDF]
, 2023 Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world.
Ali, Hafiz Shehbaz +5 more
core +1 more source
Deep-attack over the deep reinforcement learning
Knowledge-Based Systems, 2022 Accepted to Knowledge-Based ...
Yang Li 0055 +2 more
openaire +5 more sources
Deep Reinforcement Learning with Adjustments [PDF]
2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 2021 Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated problems in recent years. However, their application on real-world physical systems remains limited.
Hamed Khorasgani +3 more
openaire +2 more sources
Deep Reinforcement Learning: An Overview [PDF]
CoRR, 2017 Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant ...
Seyed Sajad Mousavi +2 more
openaire +7 more sources
Deep Reinforcement Learning for Trading [PDF]
The Journal of Financial Data Science, 2020 16 pages, 3 ...
Zihao Zhang +2 more
openaire +2 more sources
Deep Residual Reinforcement Learning
International Joint Conference on Autonomous Agents and Multiagent Systems, 2020 We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark.
Zhang, S, Boehmer, W, Whiteson, S
openaire +5 more sources
Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis [PDF]
, 2010 This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning.
Grzes, Marek
core +7 more sources