[HTML][HTML] Reinforcement learning in urban network traffic signal control: A systematic literature review
Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
Reinforcement learning methods for computation offloading: a systematic review
Z Zabihi, AM Eftekhari Moghadam… - ACM Computing …, 2023 - dl.acm.org
Today, cloud computation offloading may not be an appropriate solution for delay-sensitive
applications due to the long distance between end-devices and remote datacenters. In …
applications due to the long distance between end-devices and remote datacenters. In …
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Abstract Generative Adversarial Networks (GANs) excel at creating realistic images with
complex models for which maximum likelihood is infeasible. However, the convergence of …
complex models for which maximum likelihood is infeasible. However, the convergence of …
Actor-critic algorithms
We propose and analyze a class of actor-critic algorithms for simulation-based optimization
of a Markov decision process over a parameterized family of randomized stationary policies …
of a Markov decision process over a parameterized family of randomized stationary policies …
Stochastic Mechanics Applications of
A Board - 2003 - Springer
The original work in recursive stochastic algorithms was by Robbins and Monro, who
developed and analyzed a recursive procedure for finding the root of a real-valued function …
developed and analyzed a recursive procedure for finding the root of a real-valued function …
Mildly conservative q-learning for offline reinforcement learning
Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …
without continually interacting with the environment. The distribution shift between the …
A survey of actor-critic reinforcement learning: Standard and natural policy gradients
Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the
reinforcement learning framework. Their advantage of being able to search for optimal …
reinforcement learning framework. Their advantage of being able to search for optimal …
[BOOK][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 …
Closing the gap: Tighter analysis of alternating stochastic gradient methods for bilevel problems
Stochastic nested optimization, including stochastic compositional, min-max, and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …
optimization, is gaining popularity in many machine learning applications. While the three …
[BOOK][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 …