Reinforcement Learning and Deep RL Python (Theory and Projects)
A complete guide to reinforcement and deep reinforcement learning in Python with theory and projects
Created by AI Sciences
Explore the fundamentals and advanced techniques of reinforcement learning and deep reinforcement learning using Python. Gain hands-on experience by working through real-world projects that solidify both your theoretical understanding and practical skills. Develop the ability to apply RL concepts to solve complex problems across various domains.
Packt | Sep 2022 | 856 min
What You Will Learn
You will start by learning core RL concepts and gradually move to advanced deep RL methods, each paired with practical coding exercises. Through a series of guided projects, you will apply what you learn to real-world problems, reinforcing your understanding with hands-on practice. Explanations are concise and focus on making complex ideas approachable.
Key Features
- Master Q-learning, SARSA, and deep RL algorithms with clear Python examples
- Build projects like Frozen Lake, Cart-Pole, Car Racing, and Trading Bots from scratch
- Understand how to tune hyperparameters and apply neural networks in RL scenarios
Target Audience
This content is ideal for Python programmers with basic coding experience who want to dive into reinforcement learning and deep RL. If you are looking to build intelligent solutions, understand RL theory before coding, or apply these techniques to real projects, you will find this material valuable. A willingness to learn and experiment is all you need.
