BAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control
BAPR proposes Bayesian amnesic piecewise-robust reinforcement learning for non-stationary control by combining Bayesian Online Change Detection with robust ensemble SAC. It adapts policy conservatism after detecting regime changes, using a belief distribution to manage uncertainty. The method maintains an operator-level contraction argument under frozen parameters and uses context conditioning to identify modes without needing labels.