Publications associées à Prioritized Phase Split Optimization for Coordinated Traffic Signal Control in Urban Network Using Deep Reinforcement Learning

Publications associées (6)

Policy Evaluation in Decentralized POMDPs With Belief Sharing

Most works on multi-agent reinforcement learning focus on scenarios where the state of the environment is fully observable. In this work, we consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state dire ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Maria-Luiza Vladarean

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

2022

Real-time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters

Mario Paolone, Sidi-Rachid Cherkaoui, Antonio Zecchino, Zhao Yuan

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. ...

2021

Can Dynamic Priority Be Pareto Improving?

Nikolaos Geroliminis, Raphael Ali Francis Lamotte

Traditional priority schemes reduce delays for some by increasing those of others. Yet, this might not be a necessity. Several works published over the last two decades have shown for a stylized set-up with homogeneous users that dynamic priority scheme ma ...

2020

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Wulfram Gerstner, Johanni Michael Brea, Dane Sterling Corneil

Modern reinforcement learning algorithms reach super-human performance on many board and video games, but they are sample inefficient, i.e. they typically require significantly more playing experience than humans to reach an equal performance level. To imp ...

2018

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming

Daniel Kuhn, John Lygeros, Peyman Mohajerin Esfahani, Tobias Sutter

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex programs ...

SIAM Publications2018