TheAlgorithms
added multi armed bandit problem with three strategies to solve it
Describe your change:
- [x] Add an algorithm?
- [ ] Fix a bug or typo in an existing algorithm?
- [ ] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
- [ ] Documentation change?
Checklist:
- [x] I have read CONTRIBUTING.md.
- [x] This pull request is all my own work -- I have not plagiarized.
- [x] I know that pull requests will not be merged if they fail the automated tests.
- [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
- [x] All new Python files are placed inside an existing directory.
- [x] All filenames are in all lowercase characters with no spaces or dashes.
- [x] All functions and variable names follow Python naming conventions.
- [x] All function parameters and return values are annotated with Python type hints.
- [x] All functions have doctests that pass the automated testing.
- [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
- [x] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
What is added?
Multi-armed bandits (MAB) represent a class of sequential decision-making problems, where an agent chooses from multiple actions (or "arms") with uncertain rewards, aiming to maximize cumulative reward through balancing exploration (gathering information about each arm) and exploitation (leveraging known rewarding arms). It's one of the foundational algorithms in reinforcement learning and optimization contexts, as it models fundamental exploration-exploitation trade-offs that underpin decision-making processes. MAB algorithms, such as the epsilon-greedy, Upper Confidence Bound (UCB), and Thompson Sampling, find widespread applications across recommendation systems, adaptive clinical trials, online advertising, and resource allocation, effectively optimizing real-world decisions under uncertainty with minimal data collection.
@MaximSmolskiy, Hi, hope you are well. Can you please review this PR? It seems you are the most recent active maintainer.