ucb
Here are 59 public repositories matching this topic...
Source code for Assignment 2 of COMP90051 (Semester 2 2020)
-
Updated
Oct 21, 2020 - Jupyter Notebook
[Python] Explored different Multiarmed Bandits algorithms to find the best election campaigns more effectively
-
Updated
Jan 22, 2022 - HTML
Multi Armed Bandits implementation using the Jester Dataset
-
Updated
Apr 5, 2021 - Python
We compare different policies for the checkers game using reinforcement learning algorithms.
-
Updated
Aug 24, 2020 - Python
R.I.T project
-
Updated
Jul 29, 2019 - Python
📖 Personal Solutions for CS 61B Data Structures, UC Berkeley,Spring 2018
-
Updated
Feb 20, 2022 - Java
Implementation of Multi-Armed Bandit (MAB) algorithms UCB and Epsilon-Greedy. MAB is a class of problems in reinforcement learning where an agent learns to choose actions from a set of arms, each associated with an unknown reward distribution. UCB and Epsilon-Greedy are popular algorithms for solving MAB problems.
-
Updated
Mar 26, 2023 - Python
-
Updated
Apr 26, 2023 - Python
Complete Tutorial Guide with Code for learning ML
-
Updated
Apr 21, 2023 - Python
This is a sample code written in R that compares Thompson Sampling and UCB for three available arms sampled from a bernoulli distribution.
-
Updated
Feb 5, 2021 - R
LoRa@FIIT algorithms comparison using jupyter notebooks
-
Updated
Dec 10, 2023 - Jupyter Notebook
-
Updated
Jan 26, 2022 - Jupyter Notebook
Python package for Unity Cloud Build api
-
Updated
Sep 12, 2020 - Python
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
-
Updated
Apr 17, 2022 - Python
Reinforcement learning used in the game of pong
-
Updated
May 20, 2024 - C++
Guidebook for CS70, Discrete Math and Probability Theory at Berkeley
-
Updated
Jan 12, 2023
Improve this page
Add a description, image, and links to the ucb topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the ucb topic, visit your repo's landing page and select "manage topics."