Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning

arXiv:2601. 19624v3 Announce Type: replace-cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

• reinforcement-learning