Research preparation advice

Hi, I’ll be doing research at Mila Quebec this summer, and I’d love some advice on how to and what to prepare.

The topic is Causal models for continual reinforcement learning. More specifically, the project hypothesizes that agents whose goal is to maximize empowerment gains will construct causal models of their actions and generalize better in agentic systems.

For some background, I’m a last semester McGill undergraduate majoring in Statistics and Software Eng. I’ve done courses about:
-PGMs: Learning and inference in Bayesian and Markov networks, KL divergence, message passing, MCMC
-Applied machine learning: Logistic regression, CNN, DNN, transformers
-RL: PPO, RLHF, model-based, hierarchical, continual
and standard undergraduate level stats and cs courses.

Based on this, what do you guys think I should prepare?

I’m definitely thinking some information theory at least

Thanks in advance!