I am familiar with few deep learning models and I understand how (little bit why) CNN/RNN works. But I still cannot make sense of new research papers. I want to dive deeper into field of deep learning specifically reinforcement learning. How should I approach learning mathematics as a self-learner?

My Background:

• I'm familiar with high school mathematics (basic integration/differentiation/limits etc.).
• I know just enough of deep learning to understand what different layers/optimization functions means.
• I have participated in one kaggle competition Deepfake Detection Challenge and got 132th place on leaderboard.
• I have fairly good grasp on programming.

What Do I want to achieve:

• I want to be able to read research papers and understand what they're trying to convey with intuition.
• Able to reason about new reinforcement learning algorithms.
• Design new algorithms

Currently What I'm planning to do (in order):

• calculus from CALCULUS WITH ANALYTIC GEOMETRY
• Linear algebra book by Gilbert S.
• Pattern Recognition and Machine Learning Book by Christopher Bishop
• Probability (suggestion from comments)
• Statistics (suggestion from comments)
• Optimization (suggestion from comments)
• -> All with help from various MIT OCW and youtube videos

I value every suggestion and I'm ready to devote however much time it takes. Also I understand that I'm aiming too high here so I'm open to any advice related.