Conventional wisdom dictates that learning rate should be in the stable regime so gradient-based algorithms don't blow up. This letter introduces a simple scenario where an unstably large scheme leads to super fast convergence, with convergence depending only logarithmically on condition number of problem. Our uses Cyclical Learning Rate we periodically take one unstable step and several small ...

anxiety disorders are the most common reasons for referring to specialized clinics. if the response to stress changed, anxiety can be greatly controlled. the most obvious effect of stress occurs on circulatory system especially through sweating. the electrical conductivity of skin or in other words galvanic skin response (gsr) which is dependent on stress level is used; beside this parameter pe...

In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning. For a polynomial learning rate, one which is 1/t at time t where ω ∈ (1/2, 1), we show that the convergence rate is polynomial in 1/(1− γ), where γ is the discount factor. In contrast we show that for a linear learning rate, one whi...