In the previous blog post we discussed some theory of how to select optimal and possibly optimal interventions in a causal framework. For those interested in the decision science, this blog post may be more inspiring. This next task involves applying counterfactual quantities to boost learning performance. This is clearly very important for an RL agent where its entire learning mechanism is based on interventions in a system. What if intervention isn’t possible? 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?
Counterfactual Decision Making
A key feature of causal inference is its ability to deal with counterfactual queries. Reinforcement learning, by its nature, deals with interventional quantities in a trial-and-error style of learning.…