This repository contains the Python codes for the numerical simulations of the following paper (we also aim to extend this package with more MP-MAB algorithms in the future):

[Wang2021] Wenbo Wang, Amir Leshem, Dusit Niyato and Zhu Han, "Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game," to appear in IEEE Transactions on Wireless Communications, 2021.

This project currently implements the multi-player multi-armed bandit (MP-MAB) algorithms proposed in the following (preprint) papers:

[Wang2021] Wenbo Wang, Amir Leshem, Dusit Niyato and Zhu Han, "Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game," to appear in IEEE Transactions on Wireless Communications, 2021.

[Rosenski2016] Rosenski, J., Shamir, O., & Szlak, L. (2016, June). Multi-player bandits–a musical chairs approach. In International Conference on Machine Learning (pp. 155-163).

[Bistritz2018] Bistritz, I., & Leshem, A. (2018). Distributed multi-player bandits-a game of thrones approach. In Advances in Neural Information Processing Systems (pp. 7222-7232).

[Boursier2019] E. Boursier and V. Perchet, “Sic-mmab: synchronisation involves communication in multiplayer multi-armed bandits,” in Advances in Neural Information Processing Systems, Vancouver CANADA, Dec. 2019, pp. 12 071–12 080

[Sumit2019] Sumit J. Darak and Manjesh K. Hanawal, "Multi-player multi-armed bandits for stable allocation in # heterogeneous ad-hoc networks", IEEE JSAC oct. 2019.

[Tibrewal2019] Tibrewal, H., Patchala, S., Manjesh K. Hanawal and Sumit J. Darak (2019). "Multiplayer multiarmed bandits for optimal assignment in heterogeneous networks," arXiv preprint arXiv:1901.03868.

Note: [Tibrewal2019] is provided as an alternative algorithm for comparison with [Sumit2019].

This Python package contains the following modules:

1. Simulation entrances: main_MPMAB.py and main_MPMAB_IoT_Simu.py

2. Configurations for simulation: simu_config.py

• We suggest the simulations to be run in the parallel mode for a small size of network and non-parallel mode for larger network (due to possible memory limits).

3. Simulation engine: GameEvaluator.py

• Environment generator:
  • MPMAB.py: MAB environment simulator for known distribution of the arm values,
  • HetNetSimulator.py: for a home-brewed IoT HetNet environment,
  • Arms.py: arm simulator for known distribution.
• Algorithm organizer:
  • MABAlgorithms.py: implementations of the centralized Hugarian algorithm, [Wang2020] (TrialandError), [Rosenski2016] (MusicalChairs) and [Bistritz2018] (GameofThrone),
  • MABAlgorithms2.py: implementations of [Sumit2019] (SOC),
  • MABAlgorithms2a.py: implementations of[Tibrewal2019] (ESE, cf. SOC).
  • MABAlgorithms3.py: implementation of [Boursier2019] (SIC-MMAB)
• Player simulator:
  • Players.py, Players2.py, Players2a.py and Players3.py: the player simulators corresponding to different MABAlgorithms*.py files
• Result recorder: PlayResult.py

4. Miscellaneous utilities: plotutils.py, envutils.py, loggingutils.py