Evolutionary AI: Deep Reinforcement Learning in Python (v2)

Discover the cutting edge of reinforcement learning with a fresh, evolutionary approach. In this course, you’ll master Evolution Strategies (ES) and Augmented Random Search (ARS) - two powerful algorithms that bypass many of the challenges of traditional deep RL, while still achieving state-of-the-art results.Unlike gradient-heavy methods, these algorithms are simple, scalable, and surprisingly effective. You’ll implement them from scratch in Python and apply them to exciting real-world problems:MuJoCo Environments: Train agents to walk, run, and jump in a physics-based simulation that’s widely used in robotics research. Watching your neural network–powered agent learn to control a simulated robot is one of the most rewarding experiences in reinforcement learning.Algorithmic Trading: Apply evolutionary RL to trading strategies, where direct gradients are difficult to define. You’ll see how these algorithms adapt naturally to noisy, complex environments like financial markets.By the end of this course, you’ll have:A deep understanding of ES and ARS, and how they compare to policy gradients and Q-learning.Working Python implementations you can extend to your own projects.The skills to leverage evolutionary AI in domains ranging from robotics to finance.If you’re ready to move beyond the usual deep RL algorithms and explore approaches that are elegant, efficient, and highly practical, this course is for you.Tools and LibrariesPython (with full code walkthroughs)Gymnasium (formerly OpenAI Gym)NumPy, MatplotlibWhy This Course?Version 2 updates: Streamlined content, clearer explanations, and updated libraries.Real implementations: Go beyond the