SUPR
Federated Reinforcement learning
Dnr:

NAISS 2024/22-465

Type:

NAISS Small Compute

Principal Investigator:

Arunava Naha

Affiliation:

Uppsala universitet

Start Date:

2024-04-02

End Date:

2025-05-01

Primary Classification:

10207: Computer Vision and Robotics (Autonomous Systems)

Webpage:

Allocation

Abstract

In this project, we will study federated reinforcement learning for closed loop control applications. We need to evaluate and study the performance of the developed algorithms. FRL requires emulating multiple clients (each with one gpu and two copies of neural network for actor and critic networks) and a server (with one gpu and two copies of neural network for actor and critic networks). Such an application is supposed to generate a lot of simulated data and requires high amount of gpu based computations.