Causal Deep Reinforcement Learning Using Observational Data

Understand

Deep reinforcement learning (DRL) requires the collection of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field.

• Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world.
• However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders).
• In this paper, we propose two deconfounding methods in DRL to address this problem.