From Static Policies to Adaptive Priors in Offline Reinforcement Learning
This position paper argues that, in such settings, the objective of offline RL should extend beyond immediate deployment and instead prioritize learning adaptive policy priors: policies that preserve the capacity to improve during subsequent interaction through memory, exploration, and self-correction.
Tian-Wei Ni, Vineet Jain, Akash Karthikeyan et al.
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