Before revision:  Another dominant component especially in continuous domains is the policy gradient method which models and optimizes the policy directly. However, when Q functions are approximated with neural networks, their landscapes can be complex and therefore mislead the local gradient. In this work , we propose a self-regularized and self-guided actor-critic method .
After revision:  Our main contribution in this work is a self-regularized and self-guided actor-critic method .