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 Libraries - Python (with full code walkthroughs) - Gymnasium (formerly OpenAI Gym) - NumPy, Matplotlib Why This Course? - Version 2 updates: Streamlined content, clearer explanations, and updated libraries. - Real implementations: Go beyond theory by building working agents — no black boxes. - For all levels: Includes a dedicated review section for beginners and deep dives for advanced learners. - Proven structure: Designed by an experienced instructor who has taught thousands of students to success in AI and machine learning. Who Should Take This Course? - Data Scientists and ML Engineers who want to break into Reinforcement Learning - Students and Researchers looking to apply RL in academic or practical projects - Developers who want to build intelligent agents or AI-powered games - Anyone fascinated by how machines can learn through interaction Join thousands of learners and start mastering Reinforcement Learning today — from theory to full implementations of agents that think, learn, and play. Enroll now and take your AI skills to the next level!

What you'll learn:

• Understand and implement Evolution Strategies (ES) from scratch
• Understand and implement Augmented Random Search (ARS) from scratch
• Apply evolutionary methods to MuJoCo (physics simulation environment)
• Apply evolutionary methods to classic control reinforcement learning environments
• Apply evolutionary methods to stock trading and portfolio optimization