This section is the home of my work related things that have been lost in the process of being a researcher.
I also like to publish anything I think a few people will enjoy to read.
Open science is great!
A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric.
What I learned about deep RL and model-based learning at NeurIPs 2020.
A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.