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

In three linesAES (Adaptive Entropy Scheduling) method dynamically adjusts entropy coefficient in non-stationary RL under environment drift. Proposes square-root scaling rule based on observable non-stationarity proxy. Evaluation across 4 algorithm variants, 12 tasks, 4 drift modes: reduces performance degradation from drift and accelerates recovery after abrupt changes.

Summary generated by Claude — human-verified