🤖 AI Summary
This study addresses minimax-improved adaptation laws and learning thresholds under unknown gains in continuous-time adaptive control. Methodologically, it establishes a uniform deficit-energy inequality within a scalar Gaussian experiment framework, proposes a moving soft-thresholding feedback mechanism, and conducts the analysis through Gaussian sequential control theory. The primary contributions include the first rigorous proof of an exact quartic-logarithmic critical law, revealing phase-transition boundaries for learning and the impact of terminal cost variance. Furthermore, the work establishes the asymptotic task boundary c_gH^4 = d^2/2 and verifies that the regret bound approaches the optimal O(N^{-1/2}) rate for fixed-horizon Gaussian problems.
📝 Abstract
We study episodic continuous-time control with an unknown vector control gain, scalar state, quadratic action cost, and smooth convex terminal cost. In the scalar Gaussian experiment, let $\Delta(H)$ denote the minimax improvement over zero control and set $\delta=\sqrt2H^2-1$. We prove the critical law $$ \Delta(H)\asymp \delta^4\sqrt{\log(1/\delta)} \qquad (\delta\downarrow0), $$ with a matching lower and upper bound. The lower bound follows from a uniform deficit--energy inequality valid for fully adaptive controls with unbounded amplitudes, while a moving soft-threshold feedback attains the rate. For local parameters $\theta=N^{-1/4}h$, $|h|\le H$, we show that the normalized minimax regret over $N$ episodes is within $O(N^{-1/2})$ of a fixed-horizon Gaussian sequential control problem, without an additional dimension factor. The terminal task enters the limit only through $c_g=\operatorname{Var}(g(Z))$. The Gaussian problem exhibits an exact no-learning phase transition: $$ C_d^T(H)=TH^2/2 \iff H^4T\le d^2/2, $$ yielding the asymptotic task boundary $c_gH^4=d^2/2$.