Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem

Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem

Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning. This type of machine learning is essential for allowing systems to make decisions using only historical information without needing real-time interaction with the world.