Reinforcement Learning Study Note

less than 1 minute read


There are already a number of excellent tutorials and lectures on Reinforcement Learning (RL) available, but I often find that these do not provide enough detail or explanation of the formulas behind them. This may be because a lot of the concepts are assumed to be obvious or straightforward.

This can be a problem for someone like me who has a weak background in mathematics, but wants to gain a comprehensive understanding of RL. To address this, I am attempting to explain the theory as clearly as possible, using plenty of examples and details. Moreover, many Python RL implementations rely heavily on external libraries or are overly simplistic. Thus, I am endeavoring to bridge the gap between theory and implementation. Finally, I want to acknowledge that this work is based on numerous sources, and I am forever indebted to them.