Structure and Interpretation of Computer Programs
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Updated
Sep 12, 2020 - Python
Structure and Interpretation of Computer Programs
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
CS 61A: Structure and Interpretation of Computer Programs, Fall 2022, UC Berkeley
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.
Codes and templates for ML algorithms created, modified and optimized in Python and R.
Thompson Sampling for Bandits using UCB policy
This repo contains all the materials and solutions of UC Berkeley CS189/289A spring semester 2024
On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
Foundations Of Intelligent Learning Agents (FILA) Assignments
We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.
AI for the game "Connect Four". Available on PyPI.
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.
Repository for the course project done as part of CS-747 (Foundations of Intelligent & Learning Agents) course at IIT Bombay in Autumn 2022.
My programs during CS747 (Foundations of Intelligent and Learning Agents) Autumn 2021-22
Efficient exploration and exploitation strategies using Epsilon-Greedy, UCB1, and Thompson Sampling — with code, math, and intuition.
Python package for Unity Cloud Build api
Multi Armed Bandits implementation using the Jester Dataset
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