CSE 571 Artificial Intelligence

Python implementation of UCB, EXP3 and Epsilon greedy algorithms

Offline evaluation of multi-armed bandit algorithms

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

Machine Learning based Load Balancing with RYU OpenFlow Controller

Repository Containing Comparison of two methods for dealing with Exploration-Exploitation dilemma for MultiArmed Bandits

Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.

Multi-Agent Deep Recurrent Q-Learning with Bayesian epsilon-greedy on AirSim simulator

A multi-armed bandit (MAB) simulation library in Python

Epsilon-Greedy Q-Learning in a Multi-agent Environment

See a program learn the best actions in a grid-world to get to the target cell, and even run through the grid in real-time! This is a Q-Learning implementation for 2-D grid world using both epsilon-greedy and Boltzmann exploration policies.

A multi agent reinforcement learning environment where two agents controlled by DRQNs play a custom version of the pursuit-evasion game.

Deep Recurrent Q-Network with different exploration strategies for self-driving cars (using AirSim)

Implementation of the Q-learning and SARSA algorithms to solve the CartPole-v1 environment. [Advance Machine Learning project - UniGe]

a Python-based platformer infused with Q-Learning and dynamic level creation from simple JSON files.

Public repository for a paper in UAI 2019 describing adaptive epsilon-greedy exploration using Bayesian ensembles for deep reinforcement learning.

FTRL Approach to Financial Portfolio Risk Management

RL algorithms for pygame version of Flappy Bird

This github contains a simple OpenAi Gym Maze Enviroment and (at now) a RL Algorithm to solve it.

This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.