This course is designed for learners who want to go beyond the basics and master advanced reinforcement learning algorithms. Using Python, we will implement and explore a wide range of cutting-edge techniques, including Hierarchical Reinforcement Learning (HRL), Multi-Agent RL (MARL), Safe RL, Multi-Objective RL, and Meta-Learning methods such as MAML and PILCO.
We’ll start with an optional Python programming refresher, covering essential syntax, data structures, and object-oriented programming — perfect if you want to brush up before diving into advanced topics.
From there, you’ll work through practical coding projects using popular frameworks like Stable-Baselines3, PyQlearning, and TF-Agents. These projects include CartPole with PPO and DQN, predator–prey simulations, traveling salesman optimization with simulated annealing, portfolio management, and adaptive market planning.
By the end of the course, you will:
- Understand and implement advanced RL algorithms from scratch
- Apply RL to multi-agent, multi-objective, and safety-critical environments
- Use Python and major RL libraries to solve real-world problems
- Build a portfolio of projects to showcase your skills
Whether you’re a data scientist, machine learning engineer, or researcher, this course will give you the tools to push beyond standard RL and apply sophisticated decision-making systems to your work. You’ll be ready to tackle complex environments and design innovative AI solutions.
- This course is ideal for data scientists, machine learning engineers, AI researchers, and developers who want to go beyond standard reinforcement learning. It’s also valuable for graduate students and professionals aiming to apply advanced RL techniques to real-world problems in finance, operations, robotics, or decision-making systems.
