Learning path from Q-learning to TD3 (course suggestions?)

I’m a graduate research assistant working on autonomous vehicle–related research. I was given an existing codebase with folders like Q-learning / DQN / DDPG / TD3, and I’m expected to replicate and work with TD3.

The problem is that I currently have: Basic Python skills, very limited Intro-level understanding of RL (Q-learning, DQN) and almost no exposure to actor–critic methods

I’m looking for a clear learning roadmap that builds knowledge from tabular Q-learning → DQN → policy gradients → DDPG → TD3 (and beyond).

I’m not trying to go deep into math proofs right now. What I need are:

• Courses / playlists / tutorials that build intuition and implementation skills
• A practical sequence that prepares someone to understand and modify TD3 code

If you had to start from basic RL and reach TD3 efficiently, what resources or course order would you recommend?