Causal inference in the social sciences
GW Imbens - Annual Review of Statistics and Its Application, 2024 - annualreviews.org
Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …
settings. In many cases, however, these causal effects are not known to the decision makers …
Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence
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 …
Things (IoT) applications and services, spanning from recommendation systems and speech …
Is pessimism provably efficient for offline rl?
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 dataset collected a priori. Due to the lack of further interactions with the environment …
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
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 …
from existing datasets followed by fast online fine-tuning with limited interaction. However …
The statistical complexity of interactive decision making
A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
Provably efficient reinforcement learning with linear function approximation
Abstract Modern Reinforcement Learning (RL) is commonly applied to practical problems
with an enormous number of states, where\emph {function approximation} must be deployed …
with an enormous number of states, where\emph {function approximation} must be deployed …
[图书][B] Control systems and reinforcement learning
S Meyn - 2022 - books.google.com
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 …
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
Nearly minimax optimal reinforcement learning for linear mixture markov decision processes
We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
When is partially observable reinforcement learning not scary?
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 …
agents learn to make a sequence of decisions despite lacking complete information about …
River: machine learning for streaming data in python
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 …
provides multiple state-of-the-art learning methods, data generators/transformers …