AINeutralarXiv – CS AI · 5h ago6/10
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Extending Differential Temporal Difference Methods for Episodic Problems
Researchers propose a generalization of differential temporal difference (TD) methods that extends their applicability from infinite-horizon to episodic reinforcement learning problems. By addressing how reward centering affects policy optimization in episodic settings, the work maintains theoretical guarantees while empirically demonstrating improved sample efficiency across multiple algorithms and environments.