arxiv
PublishedApril 30, 2026 at 4:00 AM
▲bullish
Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
Publisher summary· verbatim
arXiv:2508.19900v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables learning effective policies from fixed datasets without any environment interaction. Existing methods typically employ policy constraints to mitigate the distribution shift encountered during offline RL t
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Originally published on arxiv ↗