Developed a reinforcement learning framework using Deep Q-Networks (DQN) to optimize scheduling in Wireless Sensor Networks (WSN), enhancing energy efficiency and state estimation through a custom simulation environment.

Training a vision-based agent with the Deep Q Learning Network (DQN) in Atari's Breakout environment, implementation in Tensorflow.

reinforcement learning DQN method to solve OpenAi Gym "LunarLander-v2" by usnig a Deep Neuralnetwork

A program to train an agent on a simple tile based environment using deep Q-networks (Neural Networks and Q-learning) and a program to train an agent to play the Breakout ATARI game.

A deep Q-network solution of the CartPole problem, prepared for the Collabor8.1 conference.