AI RESEARCH
Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
arXiv CS.LG
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ArXi:2601.19624v2 Announce Type: replace Real-world reinforcement learning often faces environment drift, but most existing methods rely on static entropy coefficients/target entropy, causing over-exploration during stable periods and under-exploration after drift, and leaving unanswered the principled question of how exploration intensity should scale with drift magnitude.