In the previous blog post we discussed and motivated the need for a causal approach to reinforcement learning. We argued that reinforcement learning naturally falls on the interventional rung of the ladder of causation. In this blog post we’ll develop some ideas necessary for understanding the material covered in this series. This might get quite technical, but don’t worry. There is still always something to take away. Let’s begin.
- Causal Reinforcement Learning
- Preliminaries for CRL
- Coming soon
As you probably recall from high school, probability and statistics are almost entirely formulated on the idea of drawing random samples from an experiment. One imagines observing realisations of outcomes from some set of possibilities when drawing from an assortment of independent and identically distributed (i.i.d.) events. In reality, this assumption of i.i.d. events fails in many situations. Consider shifting some distribution of events or intervening in the system. This failure of an often fundamental assumption in statistics is one reason for the causal approach we shall develop.…