Deep Reinforcement Learning

Develop a deep reinforcement learning model that can make decisions in a complex environment, such as a game or a robotic system. The model will learn from trial and error by interacting with the environment and receiving rewards or penalties for its actions. The goal is to train the model to maximize the cumulative reward over time. This project will not only help you understand deep reinforcement learning but also provide a foundation for more advanced topics in AI. Future enhancements could include applying the model to real-world problems, such as autonomous vehicles or smart homes, and exploring different reinforcement learning algorithms. The project will be built using Python and deep learning libraries such as TensorFlow or PyTorch.