CSE 571 Artificial Intelligence
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Updated
Jan 3, 2018 - Python
CSE 571 Artificial Intelligence
Python implementation of UCB, EXP3 and Epsilon greedy algorithms
Collection of Artificial Intelligence Algorithms implemented on various problems
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
implement basic and contextual MAB algorithms for recommendation system
This repo contains implementations of algorithms such a Q-learning, SARSA, TD, Policy gradient
Machine Learning based Load Balancing with RYU OpenFlow Controller
🎮 포켓몬 길찾기 게임 (광운대학교 컴퓨터정보공학부 고급C프로그래밍 팀프로젝트)
Offline evaluation of multi-armed bandit algorithms
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is fo…
ϵ-shotgun: ϵ-greedy Batch Bayesian Optimisation
RL algorithms for pygame version of Flappy Bird
A content-based music recommendation system, that suggests playlists made from the locally stored songs, and updates its suggestions based on the user feedback using non-stationary Bayesian reinforcement learning. Created using React and the Electron.js framework.
3rd Place winning solution on Kaggle Data Science for Good Competition
parameter optimization of a reinforcement learning deep Q network with memory replay buffer using genetic algorithm in the snake game. base code for snake env from codecamp
Problem Statement Perform clustering (Hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters
Improved Bot Learning process on Atari games by using Transfer Learning. An Extension of Playing Atari with Reinforcement Learning. Part of CS677 NJIT Final Project.
Reinforcement Learning with tabular methods: TD-learning (Q-learning and SARSA) and MENACE-like approach applied to a Rubik's cube with a move set restricted to 180-degree turns.
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