Reinforcement Learning for Stochastic Networks, Toulouse

Session

Parallel session: Reinforcement learning for real-life applications

Jun 21, 2024, 11:00 AM

A001 (ENSEEIHT)

43. Non-Stationary Gradient Descent for Optimal Auto-Scaling in Serverless Platforms

Dr Bruno Gaujal (Inria)

6/21/24, 11:00 AM

To efficiently manage serverless computing platforms, a key aspect is the auto-scaling of services, i.e., the set of computational resources allocated to a service adapts over time as a function of the traffic demand. The objective is to find a compromise between user-perceived performance and energy consumption. In this paper, we consider the "scale-per-request" auto-scaling pattern and...

58. A Deep-Learning Approach to High-Dimensional Impulse Control with Applications to Inventory Management

Wouter Johannes van Eekelen (University of Chicago, Booth School of Business)

6/21/24, 11:30 AM

We consider impulse control problems where the system controller can intervene in the state process by means of jumps in the underlying state space. So far, it has not been possible to efficiently solve these problems numerically in high dimensions due to the dreaded “curse of dimensionality.” To tackle this challenge, we introduce a novel deep-learning framework. Grounded in the theory of...

65. Using Reinforcement and Optimization for grid operation - A solution to the L2PRN 23 competition

Nicolas Lair (Artelys)

6/21/24, 12:00 PM

Context

In the context of the emerging risks faced by the electrical grid, a number of initiatives have been launched by major players to devise innovative ways of operating the power grid based on optimization and machine learning [1-3].

Among them, RTE, French TSO, is animating the L2RPN competition (Learning to Run a Power Network) [2] to encourage the development of solutions based...