Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech …
Y Jin, Z Yang, Z Wang - International Conference on …, 2021 - proceedings.mlr.press
We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. Due to the lack of further interactions with the environment …
A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However …
A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
C Jin, Z Yang, Z Wang… - Conference on learning …, 2020 - proceedings.mlr.press
Abstract Modern Reinforcement Learning (RL) is commonly applied to practical problems with an enormous number of states, where\emph {function approximation} must be deployed …
A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
We study reinforcement learning (RL) with linear function approximation where the underlying transition probability kernel of the Markov decision process (MDP) is a linear …
Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about …
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers …