Successor Representation

Successor representation (SR) is a reinforcement learning framework aiming to efficiently learn and generalize value functions by decoupling predictions of future state occupancy from reward signals. Current research focuses on improving SR's application in various contexts, including multi-task learning, safe reinforcement learning, and exploration strategies, often employing neural network architectures and algorithms like Kalman filtering to enhance learning speed and robustness. This approach holds significant promise for advancing reinforcement learning's sample efficiency and generalization capabilities, impacting fields such as robotics and AI safety through improved decision-making in complex, uncertain environments.