Many authors have introduced matrix gain enhancements to reinforcement learning algorithms over recent decades.   A new approach is introduced that has provably optimal asymptotic covariance,  and this clearly shows in numerical experiments:  the rate of convergence is blindingly fast compared to all competitors.  The talk will survey the theory and open problems.