Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

2020-05-04 · Sergey Levine, Aviral Kumar, George Tucker, Justin Fu

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 algorithms that utilize previously collected data, without additional online data collection. Offline reinforcement learning algorithms hold tremendous promise for making it possible to turn large datasets into powerful decision making engines. Effective offline reinforcement learning methods would be able to extract policies with the maximum possible utility out of the available data, thereby allowing automation of a wide range of decision-making domains, from healthcare and education to robotics. However, the limitations of current algorithms make this difficult. We will aim to provide the reader with an understanding of these challenges, particularly in the context of modern deep reinforcement learning methods, and describe some potential solutions that have been explored in recent work to mitigate these challenges, along with recent applications, and a discussion of perspectives on open problems in the field.

📄 PDF Abstract BibTeX arXiv:2005.01643

Code (3)

asjad99/Reinforcement-Learning

massquantity/DBRL pytorch

ray-project/ray/tree/master/rllib

Tasks

Decision MakingDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

2024-02-21 · Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei 외

Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. Similarly, data-driven decision-making an…

Decision Making Imitation Learning Offline RL Sequential Decision Making

Deep reinforcement learning in medical imaging: A literature review

2021-03-05 · S. Kevin Zhou, Hoang Ngan Le, Khoa Luu, Hien V. Nguyen 외

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks. Recent works …

Deep Reinforcement Learning Lesion Detection Medical Image Analysis Miscellaneous+4

Reinforcement Learning in Practice: Opportunities and Challenges

2022-02-23 · Yuxi Li

This article is a gentle discussion about the field of reinforcement learning in practice, about opportunities and challenges, touching a broad range of topics, with perspectives and without technical details. The articl…

Combinatorial Optimization Meta-Learning Recommendation Systems reinforcement-learning+2

Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning

2019-08-25 · Xudong Sun, Bernd Bischl

Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations. Reviews and comparisons on recent …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+2

Open-Domain Question Answering

2020-07-01 · ACL 2020 6 · Danqi Chen, Wen-tau Yih

This tutorial provides a comprehensive and coherent overview of cutting-edge research in open-domain question answering (QA), the task of answering questions using a large collection of documents of diversified topics. W…

Open-Domain Question Answering Question Answering