Reinforcement Learning Without Temporal Difference Learning

A UC Berkeley BAIR blog post proposes a reinforcement learning algorithm that replaces temporal difference (TD) learning with a divide-and-conquer paradigm, aiming to improve scalability for long-horizon tasks in off-policy RL settings.

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1. AnnouncementNov 1, 2025, 09:00 AM 84%

   UC Berkeley BAIR introduces a divide-and-conquer RL algorithm to replace TD learning for improved scalability in off-policy settings.

   A UC Berkeley BAIR blog post proposes a reinforcement learning algorithm that replaces temporal difference (TD) learning with a divide-and-conquer paradigm, aiming to improve scalability for long-horizon tasks in off-policy RL settings.

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