A Unified Framework for Zero-Shot Reinforcement Learning
The work creates a formal framework for zero-shot reinforcement learning by organizing existing methods into representation and learning paradigm levels. It proposes a unified error decomposition—into inference, reward, and approximation components—to rigorously analyze how learned representations enable agents to solve new tasks without prior training.