Deep Reinforcement Learning is one of the most advanced branches of Artificial Intelligence and the foundation of systems capable of learning to make complex decisions autonomously. In this course you'll learn from the fundamentals to the implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3), one of the most powerful algorithms for continuous control environments, using Python, PyTorch and Google Colab.

We'll start by building a solid foundation in the essential concepts of Reinforcement Learning. You'll understand how Q-Learning, Deep Q-Learning (DQN), Policy Gradient and Actor-Critic work, discovering when to use each technique and how they evolve to give rise to state-of-the-art algorithms like TD3. All of it explained with a visual, intuitive and practice-oriented approach.

Once you've mastered the fundamentals, we'll dive deeper into the theory behind Twin Delayed DDPG (TD3). You'll analyze step by step how the actor and critic interact, why the dual critic improves training stability, and how the algorithm's different strategies enable more robust and efficient learning in complex control problems.

Finally, you'll implement TD3 from scratch with PyTorch, developing each component through hands-on exercises and guided programming sessions. You'll train intelligent agents in advanced simulations, like humanoids and quadruped robots capable of learning to walk and run autonomously. Plus, you'll work entirely in Google Colab, with no need to install software or set up environments, so you can focus solely on learning and building high-level Artificial Intelligence models.