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It is a repository containing the implementation of combinatorial multi-armed bandit algorithm with Thompson Sampling to solve combinatorial optimization problems involving single agent as well as multiple agents in a decentralized environment.

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sharyalZ/D-CMAB-TS

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Decentralized-Combinatorial Multi-Armed Bandit-Thompson Sampling (D-CMAB-TS)

It is a repository containing the implementation of combinatorial multi-armed bandit algorithm with Thompson Sampling to solve combinatorial optimization problems involving single agent as well as multiple agents in a decentralized environment. Full algorithm can be found here.


Architecture

The repository includes following files:

  • src: Contains the implementation of the learning agent class.
  • Jupiter Notebook Examples: Contains Jupyter Notebook examples utilizing the implemented learning agent to solve single agent and multi-agent combinatorial optimization problems in a decentralized manner.

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For any further information, you can contact me at sharyal.zafar@ens-rennes.fr.

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It is a repository containing the implementation of combinatorial multi-armed bandit algorithm with Thompson Sampling to solve combinatorial optimization problems involving single agent as well as multiple agents in a decentralized environment.

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