Multi Armed Bandits implementation using the Jester Dataset
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Updated
Apr 5, 2021 - Python
Multi Armed Bandits implementation using the Jester Dataset
We compare different policies for the checkers game using reinforcement learning algorithms.
R.I.T project
Complete Tutorial Guide with Code for learning ML
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.
Python package for Unity Cloud Build api
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
Thompson is Python package to evaluate the multi-armed bandit problem. In addition to thompson, Upper Confidence Bound (UCB) algorithm, and randomized results are also implemented.
On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
Foundations Of Intelligent Learning Agents (FILA) Assignments
AI for the game "Connect Four". Available on PyPI.
Codes and templates for ML algorithms created, modified and optimized in Python and R.
Thompson Sampling for Bandits using UCB policy
Author's implementation of the paper Correlated Age-of-Information Bandits.
Multi-armed bandit algorithm with tensorflow and 11 policies
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