Exploring Invariant Causal Predictions in Reinforcement Learning

This chapter focuses on invariant causal predictions and their significance in understanding causal relationships within reinforcement learning. It discusses the comparison of linear models with deep learning in discovering predictive relationships, while addressing challenges in training agents without prior knowledge. The chapter also highlights recent advancements in multi-task reinforcement learning and the importance of contextual information for improving agent performance.