Source code for Assignment 2 of COMP90051 (Semester 2 2020)
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Updated
Oct 21, 2020 - Jupyter Notebook
Source code for Assignment 2 of COMP90051 (Semester 2 2020)
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
Multi Armed Bandits implementation using the Jester Dataset
We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.
R.I.T project
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.
LoRa@FIIT algorithms comparison using jupyter notebooks
Foundations Of Intelligent Learning Agents (FILA) Assignments
Chapter wise implementation & analysis of all the algorithms in RL : An Intoduction by Richard S. Sutton and Andrew G. Barto
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
Reinforcement learning used in the game of pong
Complete Tutorial Guide with Code for learning ML
CS70 Homework and Discussion Solutions
Train and test your IA's using these samples in the machine learning field.
Bandit algorithms in OCaml
This is a sample code written in R that compares Thompson Sampling and UCB for three available arms sampled from a bernoulli distribution.
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