Abstract
In reinforcement learning (RL), an agent learns an environment through hit and trail. This behavior allows the agent to learn in complex and difficult environments. In RL, the agent normally learns the given environment by exploring or exploiting. Most of the algorithms suffer from under exploration in the latter stage of the episodes. Recently, an off-policy algorithm called soft actor critic (SAC) is proposed that overcomes this problem by maximizing entropy as it learns the environment. In it, the agent tries to maximize entropy along with the expected discounted rewards. In SAC, the agent tries to be as random as possible while moving towards the maximum reward. This randomness allows the agent to explore the environment and stops it from getting stuck into local optima. We believe that maximizing the entropy causes the overestimation of entropy term which results in slow policy learning. This is because of the drastic change in action distribution whenever agent revisits the similar states. To overcome this problem, we propose a dual policy optimization framework, in which two independent policies are trained. Both the policies try to maximize entropy by choosing actions against the minimum entropy to reduce the overestimation. The use of two policies result in better and faster convergence. We demonstrate our approach on different well known continuous control simulated environments. Results show that our proposed technique achieves better results against state of the art SAC algorithm and learns better policies.