Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model
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Updated
Sep 8, 2021 - Python
Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model
Randomized Greedy Learning Under Full-bandit Feedback
Repository contains codes for the course CS780: Deep Reinforcement Learning
An Implementation of the N-Tuple Bandits Evolutionary Algorithm.
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
An implementation of the TME from the Reinforcement Learning course given at Sorbonne University.
🎩🤠Some Bandit Algorithms in Typescript
2024 ICML Official code
Implementation of greedy, ε-greedy and softmax methods for n-armed bandit problem
An implementation of the matching bandit algorithm in http://proceedings.mlr.press/v139/sentenac21a.html.
Vectorized bandit algorithms implemented in NumPy and Cython
Today I Learned - Reinforcement Learning
a collection of google colab notebooks with educational stuff about bandits and their variations
Ads Click-through rate using thompson sampling
Several multi-armed bandit strategies with additional holding option for smoother exploration.
The official code repo for HyperAgent for neural bandits and GPT-HyperAgent for content moderation.
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