LUNER LANDER WITH DEEP Q-LEARNING

-> This project implements a reinforcement learning agent that learns to land a spacecraft in the LunarLander-v2 environment using Deep Q-Learning (DQN). -> The agent is trained to maximize its score by successfully landing on the lunar surface while minimizing bounces and crashes. ->The LunarLander-v2 environment is part of the OpenAI Gym and simulates the landing of a spacecraft on the Moon. ->The agent uses a Deep Q-Network to learn optimal landing strategies through trial and error. ->The goal is to land the spacecraft safely on the landing pad while maximizing the score.

TABLE OF CONTENTS (1) Introduction (2) Environment Setup (3) Code Overview (4) Training the Agent (5) Results (6) Future Improvements (7) License