Reinforcement learning in healthcare: A survey
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …
making by using interaction samples of an agent with its environment and the potentially …
Deep reinforcement learning for power system applications: An overview
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …
conventional methods often meet bottlenecks when attempting to solve decision and control …
Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
Applications of deep learning and reinforcement learning to biological data
Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …
new possibilities for life scientists to gather multimodal data in various application domains …
Hamiltonian-driven adaptive dynamic programming with efficient experience replay
This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …
Precision medicine
Precision medicine seeks to maximize the quality of health care by individualizing the health-
care process to the uniquely evolving health status of each patient. This endeavor spans a …
care process to the uniquely evolving health status of each patient. This endeavor spans a …
[BOEK][B] Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions: by Warren B. Powell (ed.), Wiley (2022). Hardback. ISBN …
I Halperin - 2022 - Taylor & Francis
What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …
optimization? And finally, can it be used for applications to quantitative finance for my current …
Reinforcement learning for control: Performance, stability, and deep approximators
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
[PDF][PDF] Introduction to reinforcement learning
D Ernst, A Louette - Feuerriegel, S., Hartmann, J., Janiesch, C., and …, 2024 - damien-ernst.be
Examples:• A predictive maintenance agent for industrial equipment that analyzes sensor
data to predict failures before they happen, scheduling maintenance only when needed and …
data to predict failures before they happen, scheduling maintenance only when needed and …
[BOEK][B] Lifelong machine learning
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …
learning paradigm that continuously learns by accumulating past knowledge that it then …