This is a chatbot that I build using ChatGPT.

Gamelike car simulation playground.

Artificial Neural Network (MLP) and Deep Q-Learning Implementation from scratch, only using numpy.

I developed and trained a deep convolutional Q-learning model to enable an agent to successfully solve the Pacman gym environment.

Researching new methods to leverage Coverage and Path Planning using DeepRL

Tic Tac Toe game, designed to be used to train a Deep Neural Network via Reinforcement Learning (DQN). It can also be played by 2 humans and features a hard coded AI that never looses and will win if you do not do perfect play against it.

Deep Reinforcement Learning algorithms to play Connect4 using a combination of Supervised Learning and Reinforcement Learning

Project for AI in computer games

In this project, an artificial agent is trained to solve the Maze task using DQN algorithm

reinforcement learning for power grid optimal operations and maintenance

Training AI to play snake

Reinfocement Learning Approach to solve Shortest Path Problem.

comparison of q-learning and deep q-learning

Deep reinforcement Q learning model to find the treasure at a fixed location from a random location in a maze

Using the "Advantage Actor Critic(A2C)" Reinforcement Learning method, the 'Agent' is trained to play Atari's Breakout.

Deep Q-learning for playing flappy bird game

Experimentation on Carla Simulator for running DQN RL agents for Autonomous Driving

Dueling DQN agents in Pong game. Pong game written by @bearpaw7

Deep Q Network with TensorFlow, used to solve CartPole environment from Gym.