This paper proposes an automated trials and errors approach that combines reinforcement learning with deep learning for analog circuit sizing. Through the self-improvement learning way, the proposed method behaves like a designer, who learns from trials and derives experience, evolving itself to finally discover the sizes that satisfy the performance specification based on simulation results. In order to greatly reduce the number of simulations, we propose a symbolic filter that builds a polynomial equation system and then applies the worked out small-signal parameters to implement symbolic analysis, passing only the satisfied ones to the simulator.