Finished Python basics — what’s the correct roadmap to truly master Reinforcement Learning?

Hi everyone, I’ve recently completed Python fundamentals (syntax, OOP, NumPy, basic plotting) and I want to seriously specialize in Reinforcement Learning. I’m not looking for a quick overview or surface-level tutorials — my goal is to properly master RL, both conceptually and practically, and understand how it’s used in real systems. I’d really appreciate guidance on: The right learning order for RL (math → theory → algorithms → deep RL) Which algorithms are must-learn vs nice-to-know How deep I should go into math as a beginner Which libraries/frameworks are actually used today (Gymnasium, PyTorch, Stable-Baselines, etc.) How to move from toy environments → real-world or research-level RL Common mistakes beginners make when learning RL Hi everyone, I’ve recently completed Python fundamentals (syntax, OOP, NumPy, basic plotting) and I want to seriously specialize in Reinforcement Learning. I’m not looking for a quick overview or surface-level tutorials — my goal is to properly master RL, both conceptually and practically, and understand how it’s used in real systems. I’d really appreciate guidance on: The right learning order for RL (math → theory → algorithms → deep RL) Which algorithms are must-learn vs nice-to-know How deep I should go into math as a beginner Which libraries/frameworks are actually used today (Gymnasium, PyTorch, Stable-Baselines, etc.) How to move from toy environments → real-world or research-level RL Common mistakes beginners make when learning RL