Evolutionary AI Deep Reinforcement Learning in Python (v2)
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Free Download Evolutionary AI Deep Reinforcement Learning in Python (v2)
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 12h 7m | Size: 4.42 GB
Build Artificial Intelligence (AI) agents using Evolution Strategies (ES) and Augmented Random Search (ARS)
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
Requirements
Python programming with numerical computing libraries (e.g. Numpy)
Building neural networks (backpropgation not required)
Calculus, linear algebra, probability are useful
Description
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 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 LearningStudents and Researchers looking to apply RL in academic or practical projectsDevelopers who want to build intelligent agents or AI-powered gamesAnyone fascinated by how machines can learn through interactionJoin 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!
Who this course is for
Machine Learning & AI enthusiasts who want to explore one of the most exciting fields in AI: reinforcement learning
Software developers and engineers looking to build intelligent agents that learn from experience
Quantitative finance professionals interested in applying RL to portfolio optimization and algorithmic trading
Students and researchers studying AI, computer science, or data science who want hands-on experience with real RL implementations
Game developers curious about using RL to train AI for complex behaviors and adaptive gameplay
Robotics practitioners who want to learn how agents can make sequential decisions in physical environments
Data scientists aiming to expand their toolkit beyond supervised learning / unsupervised learning
Traders and investors looking to apply cutting-edge AI methods to automated trading strategies
Entrepreneurs and hobbyists eager to experiment with advanced AI models and build projects that learn and adapt over time
Professionals switching careers into AI/ML and looking for portfolio-ready, real-world projects
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