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 …
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …
started on research on offline reinforcement learning algorithms: reinforcement learning …
Autonomous navigation of stratospheric balloons using reinforcement learning
Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …
Reinforcement knowledge graph reasoning for explainable recommendation
Recent advances in personalized recommendation have sparked great interest in the
exploitation of rich structured information provided by knowledge graphs. Unlike most …
exploitation of rich structured information provided by knowledge graphs. Unlike most …
Deep reinforcement learning: An overview
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Data-efficient off-policy policy evaluation for reinforcement learning
P Thomas, E Brunskill - International Conference on …, 2016 - proceedings.mlr.press
In this paper we present a new way of predicting the performance of a reinforcement
learning policy given historical data that may have been generated by a different policy. The …
learning policy given historical data that may have been generated by a different policy. The …
Bridging the gap between value and policy based reinforcement learning
We establish a new connection between value and policy based reinforcement learning
(RL) based on a relationship between softmax temporal value consistency and policy …
(RL) based on a relationship between softmax temporal value consistency and policy …
Conservative data sharing for multi-task offline reinforcement learning
Offline reinforcement learning (RL) algorithms have shown promising results in domains
where abundant pre-collected data is available. However, prior methods focus on solving …
where abundant pre-collected data is available. However, prior methods focus on solving …