Skip to content

Latest commit

 

History

History
15 lines (11 loc) · 684 Bytes

README.md

File metadata and controls

15 lines (11 loc) · 684 Bytes
Raw

FeedBAL

Updated (major revision 30/08/20) implementation of the FeedBack Adaptive Learning (FeedBAL) algorithm for the episodic multi-armed bandit (eMAB) setting.

How to run

  1. Install requirements in requirements.txt.
  2. To run simulation I (no dropouts) and simulation II (dropouts), set DROPOUT_PROB to 0 and 0.1, respectively, in the user_arrival_simulator.py file.
  3. Run the main.py script.

Specs of the PC used for the paper

A PC with the following specs was used for the simulations whose results/figures are presented in the paper:

CPU: Intel Core i7-7700 @ 3.60 GHz | RAM: 32 GB | OS: Ubuntu 18.10