Selector-Actor-Critic and Tuner-Actor-Critic Algorithms for Reinforcement Learning

This work presents two reinforcement learning (RL) architectures, which mimic rational humans in the way of analyzing the available information and making decisions. The proposed algorithms are called selector-actor-critic (SAC) and tuner-actor-critic (TAC). They are obtained by modifying the well known actor-critic (AC) algorithm. SAC is equipped with an actor, a critic, and a selector. The role of the selector is to determine the most...