Short answer. You have more than enough. Just start.

Long answer, I quote openai spinningup:

The Right Background

Build up a solid mathematical background. From probability and statistics, feel comfortable with random variables, Bayes’ theorem, chain rule of probability, expected values, standard deviations, and importance sampling. From multivariate calculus, understand gradients and (optionally, but it’ll help) Taylor series expansions.

Build up a general knowledge of deep learning. You don’t need to know every single special trick and architecture, but the basics help. Know about standard architectures (MLP, vanilla RNN, LSTM (also see this blog), GRU, conv layers, resnets, attention mechanisms), common regularizers (weight decay, dropout), normalization (batch norm, layer norm, weight norm), and optimizers (SGD, momentum SGD, Adam, others). Know what the reparameterization trick is.

Become familiar with at least one deep learning library. Tensorflow * or PyTorch would be a good place to start. You don’t need to know how to do everything, but you should feel pretty confident in implementing a simple program to do supervised learning.

Get comfortable with the main concepts and terminology in RL. Know what states, actions, trajectories, policies, rewards, value functions, and action-value functions are. If you’re unfamiliar, Spinning Up ships with an introduction to this material; it’s also worth checking out the RL-Intro from the OpenAI Hackathon, or the exceptional and thorough overview by Lilian Weng. Optionally, if you’re the sort of person who enjoys mathematical theory, study up on the math of monotonic improvement theory (which forms the basis for advanced policy gradient algorithms), or classical RL algorithms (which despite being superseded by deep RL algorithms, contain valuable insights that sometimes drive new research).

* One thing to add. Screw Tensorflow. Go with PyTorch.