This repository contains an implementation of the Deep Q-Learning algorithm to solve the Lunar Lander environment from OpenAI Gym. The goal is to train an agent to land a spacecraft on a designated landing pad by applying appropriate force and adjusting the spacecraft's orientation.
The Deep Q-Learning algorithm is a reinforcement learning technique that combines Q-Learning with deep neural networks to approximate the action-value function. This allows the agent to learn an optimal policy for selecting actions in complex environments with high-dimensional state spaces.
- Python 3.x
- TensorFlow
- OpenAI Gym
- NumPy
- Pillow
- pyvirtualdisplay
- Clone the repository:
git clone https://github.com/your-repo/deep-q-learning-lunar-lander.git