Advantage estimator based on importance sampling - Quan LIU; Yubin JIANG; Zhihui HU (Tongxin xuebao)
In continuous action tasks,deep reinforcement learning usually uses Gaussian distribution as a policy function.Aiming at the problem that the Gaussian distribution policy function slows down due to the clipped action,an importance sampling advantage estimator was proposed.Based on the general advantage estimator,an importance sampling mechanism was introduced by the estimator to improve the convergence speed of the algorithm and correct the deviation of the value function caused by calculating the target strategy and action strategy ratio of the boundary action.In addition,the L parameter was introduced by ISAE which improved the reliability of the sample and limited the stability of the network parameters by limiting the range of the importance sampling rate.In order to verify the effectiveness of the ISAE,applying it to proximal policy optimization and comparing it with other algorithms on the MuJoCo platform.Experimental results show that ISAE has a faster convergence rate.