Advanced AI: Deep Reinforcement Learning in PyTorch (v2)

Are you ready to unlock the power of Reinforcement Learning (RL) and build intelligent agents that can learn and adapt on their own. Welcome to the most comprehensive, up-to-date, and practical course on Reinforcement Learning, now in its highly improved Version 2. Whether you're a student, researcher, engineer, or AI enthusiast, this course will guide you from foundational RL concepts to advanced Deep RL implementations - including building agents that can play Atari games using cutting-edge algorithms like DQN and A2C.

What You'll LearnCore RL Concepts: Understand rewards, value functions, the Bellman equation, and Markov Decision Processes (MDPs). Classical Algorithms: Master Q-Learning, TD Learning, and Monte Carlo methods. Hands-On Coding: Implement RL algorithms from scratch using Python and Gymnasium.

Deep Q-Networks (DQN): Learn how to build scalable, powerful agents using neural networks, experience replay, and target networks. Policy Gradient & A2C: Dive into advanced policy optimization techniques and learn how actor-critic methods work in practice. Atari Game AI: Use modern libraries like Stable Baselines 3 to train agents that play classic Atari games - from scratch.

Bonus Concepts: Explore evolutionary methods, entropy regularization, and performance tuning tips for real-world applications. Tools and LibrariesPython (with full code walkthroughs)Gymnasium (formerly OpenAI Gym)Stable Baselines 3NumPy, Matplotlib, PyTorch (where applicable)Why This Course. Version 2 updates: Streamlined content, clearer explanations, and updated libraries.