Efficient Offline Reinforcement Learning with ProRL

📺 Today’s recommended deep-dive video: https://www.youtube.com/watch?v=r9MxvE6SpQo Minimal Assumptions, Maximum Efficiency: A Breakthrough in Offline Reinforcement Learning Most offline reinforcement learning algorithms rely on overly restrictive assumptions like all-policy coverage or Bellman completeness. This work introduces ProRL, a primal-dual approach that…

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