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:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:
:book: [译] UCB DS100 数据科学的原理与技巧
CS 61A: Structure and Interpretation of Computer Programs, Fall 2022, UC Berkeley
All projects about ucb-61b(2014 spring), http://www.cs.berkeley.edu/~jrs/61b/index.html
UCThello - a board game demonstrator (Othello variant) with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short)
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
Multi-armed bandit algorithm with tensorflow and 11 policies
Author's implementation of the paper Correlated Age-of-Information Bandits.
All projects about ucb-cs186(fall 2013), you can get information from the course website(https://sites.google.com/site/cs186fall2013)
Thompson Sampling for Bandits using UCB policy
Oware and Ouril - traditional African Mancala games with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short)
Codes and templates for ML algorithms created, modified and optimized in Python and R.
CS70 Homework and Discussion Solutions
A mirror website for CS61A Fall 2020 with Chinese translation.
Foundations Of Intelligent Learning Agents (FILA) Assignments
On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
Alquerque - a 2 player abstract strategic perfect information traditional board game with computer AI option.
3 dimensional Four in a Row game with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short).
Implementation of 9 multi-armed bandit algorithm for the stationary stochastic environment
AI for the game "Connect Four". Available on PyPI.
Python implementation of the Hex game with AI based on MC and MCTS methods. Interactive mode with pygame.
C++ implementation of Multi-Armed Bandits (Gaussian and Bernoulli)
We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.
CS 170 Homework Solutions
Reinforcement Learning (COMP 579) Project
Chapter wise implementation & analysis of all the algorithms in RL : An Intoduction by Richard S. Sutton and Andrew G. Barto