Edge-Compatible Reinforcement Learning for Recommendations

2021-12-10 · James E. Kostas, Philip S. Thomas, Georgios Theocharous

Most reinforcement learning (RL) recommendation systems designed for edge computing must either synchronize during recommendation selection or depend on an unprincipled patchwork collection of algorithms. In this work, we build on asynchronous coagent policy gradient algorithms \citep{kostas2020asynchronous} to propose a principled solution to this problem. The class of algorithms that we propose can be distributed over the internet and run asynchronously and in real-time. When a given edge fails to respond to a request for data with sufficient speed, this is not a problem; the algorithm is designed to function and learn in the edge setting, and network issues are part of this setting. The result is a principled, theoretically grounded RL algorithm designed to be distributed in and learn in this asynchronous environment. In this work, we describe this algorithm and a proposed class of architectures in detail, and demonstrate that they work well in practice in the asynchronous setting, even as the network quality degrades.

📄 PDF Abstract BibTeX arXiv:2112.05812

Code (0)

등록된 구현이 없습니다.

Tasks

Edge-computingRecommendation Systems reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation

2024-03-25 · Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua 외

Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR.…

Language Modeling Language Modelling Large Language Model Session-Based Recommendations

Interactive Garment Recommendation with User in the Loop

2024-02-18 · Federico Becattini, Xiaolin Chen, Andrea Puccia, Haokun Wen 외

Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumption that no prior knowledge is given abou…

reinforcement-learning Reinforcement Learning

Environments for Lifelong Reinforcement Learning

2018-11-26 · Khimya Khetarpal, Shagun Sodhani, Sarath Chandar, Doina Precup

To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

2021-11-20 · Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki, Eliseo Papa

For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent. In other domains, a neurosymbolic approach of multi-hop reasoning on kno…

Drug Discovery Knowledge Graphs reinforcement-learning Reinforcement Learning+1

Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning

2020-01-27 · Xi Liu, Li Li, Ping-Chun Hsieh, Muhe Xie 외

With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In rece…

Deep Reinforcement Learning Knowledge Distillation Multi-Task Learning reinforcement-learning+2