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
- CRL Task 1: Generalised Policy Learning
- CRL Task 2: Interventions – When and Where?
- CRL Task 3: Counterfactual Decision Making
- CRL Task 4: Generalisability and Robustness
- Task 5: Learning Causal Models
- (Coming soon) Task 6: Causal Imitation Learning
- (Coming soon) Wrapping Up: Where To From Here?
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.…