Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Multi-agent deep reinforcement learning: a survey
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
Reinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …
help us find our favorite items to purchase, our friends on social networks, and our favorite …
Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
A theoretical analysis of deep Q-learning
Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …
foundation is less well understood. In this work, we make the first attempt to theoretically …
[BOOK][B] Dynamic noncooperative game theory
T Başar, GJ Olsder - 1998 - SIAM
This is the revised second edition of our 1982 book with the same title, which presents a
rather comprehensive treatment of static and dynamic noncooperative game theory, with …
rather comprehensive treatment of static and dynamic noncooperative game theory, with …
[CITATION][C] Neuro-dynamic programming
DP Bertsekas - Athena Scientific, 1996 - books.google.com
This is historically the first book that fully explained the neuro-dynamic programming/
reinforcement learning methodology, a breakthrough in the practical application of neural …
reinforcement learning methodology, a breakthrough in the practical application of neural …