Vahid Reza Niazmand

Vahid Reza Niazmand

Prospective Master student interested in Reinforcement Learning

Posts by Tags

Deep Q network

Snake Game A.I. Agent

less than 1 minute read

Published:

The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward. We are going to see how a Deep Q-Learning algorithm learns how to play snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.(link) Read more

Deep-Q_nework

sharif ai challenge RL agent

less than 1 minute read

Published:

Sharif AI Challenge is a programming competition for all who are interested in artificial intelligence. This competition is held in two phases annually. Including an online and an on-site phase in which competitors will compete in teams of three in a game designed by our technical team. The only pre-requisite to enter this competition is familiarity with programming using C++, Java or python. But obviously, knowledge of algorithmic thinking and artificial intelligence will be a great asset for any of the participating teams. The registration for online competition is free. Read more

Tensorflow

Snake Game A.I. Agent

less than 1 minute read

Published:

The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward. We are going to see how a Deep Q-Learning algorithm learns how to play snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.(link) Read more

ai

Snake Game A.I. Agent

less than 1 minute read

Published:

The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward. We are going to see how a Deep Q-Learning algorithm learns how to play snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.(link) Read more

UC berkeley Pacman A.I. Agent Project Series

less than 1 minute read

Published:

A voluntary attempt to solve UCBerkeley’s pacman AI project series problems. It starts by implementing simple search algorithms and ends with implementing Q-learning to play as the pacman agent. (link) Read more

deep learning

Hybrid Recommender System (B.Sc. Project)

less than 1 minute read

Published:

The whole system consists of different models each suitable for a different situation and it decides automatically based on some parameters which to use the most. Both ALS and DNN have their own pros and cons but in a complementary way. Using them together can be a way of solving popular recommender system challenges. link full document Read more

sharif ai challenge RL agent

less than 1 minute read

Published:

Sharif AI Challenge is a programming competition for all who are interested in artificial intelligence. This competition is held in two phases annually. Including an online and an on-site phase in which competitors will compete in teams of three in a game designed by our technical team. The only pre-requisite to enter this competition is familiarity with programming using C++, Java or python. But obviously, knowledge of algorithmic thinking and artificial intelligence will be a great asset for any of the participating teams. The registration for online competition is free. Read more

game

sharif ai challenge RL agent

less than 1 minute read

Published:

Sharif AI Challenge is a programming competition for all who are interested in artificial intelligence. This competition is held in two phases annually. Including an online and an on-site phase in which competitors will compete in teams of three in a game designed by our technical team. The only pre-requisite to enter this competition is familiarity with programming using C++, Java or python. But obviously, knowledge of algorithmic thinking and artificial intelligence will be a great asset for any of the participating teams. The registration for online competition is free. Read more

machine learning

Hybrid Recommender System (B.Sc. Project)

less than 1 minute read

Published:

The whole system consists of different models each suitable for a different situation and it decides automatically based on some parameters which to use the most. Both ALS and DNN have their own pros and cons but in a complementary way. Using them together can be a way of solving popular recommender system challenges. link full document Read more

matrix factorization

Hybrid Recommender System (B.Sc. Project)

less than 1 minute read

Published:

The whole system consists of different models each suitable for a different situation and it decides automatically based on some parameters which to use the most. Both ALS and DNN have their own pros and cons but in a complementary way. Using them together can be a way of solving popular recommender system challenges. link full document Read more

pacman

UC berkeley Pacman A.I. Agent Project Series

less than 1 minute read

Published:

A voluntary attempt to solve UCBerkeley’s pacman AI project series problems. It starts by implementing simple search algorithms and ends with implementing Q-learning to play as the pacman agent. (link) Read more

recommender system

Hybrid Recommender System (B.Sc. Project)

less than 1 minute read

Published:

The whole system consists of different models each suitable for a different situation and it decides automatically based on some parameters which to use the most. Both ALS and DNN have their own pros and cons but in a complementary way. Using them together can be a way of solving popular recommender system challenges. link full document Read more

reinforcement_learning

UC berkeley Pacman A.I. Agent Project Series

less than 1 minute read

Published:

A voluntary attempt to solve UCBerkeley’s pacman AI project series problems. It starts by implementing simple search algorithms and ends with implementing Q-learning to play as the pacman agent. (link) Read more

sharif ai challenge RL agent

less than 1 minute read

Published:

Sharif AI Challenge is a programming competition for all who are interested in artificial intelligence. This competition is held in two phases annually. Including an online and an on-site phase in which competitors will compete in teams of three in a game designed by our technical team. The only pre-requisite to enter this competition is familiarity with programming using C++, Java or python. But obviously, knowledge of algorithmic thinking and artificial intelligence will be a great asset for any of the participating teams. The registration for online competition is free. Read more

snake

Snake Game A.I. Agent

less than 1 minute read

Published:

The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward. We are going to see how a Deep Q-Learning algorithm learns how to play snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.(link) Read more

ucberkeley

UC berkeley Pacman A.I. Agent Project Series

less than 1 minute read

Published:

A voluntary attempt to solve UCBerkeley’s pacman AI project series problems. It starts by implementing simple search algorithms and ends with implementing Q-learning to play as the pacman agent. (link) Read more