Actor Double Critic Architecture for Dialogue System

Most of the recent dialogue policy learning ‎methods are based on reinforcement learning (RL). However, the basic RL ‎algorithms like deep Q-network, have drawbacks in environments with ‎large state and action spaces such as dialogue systems. Most of the ‎policy-based methods are slow, cause of the estimating of the action value ‎using the computation of the sum of the discounted rewards for each ‎action. In value-based RL methods, function approximation errors lead to ‎overestimation in value estimation and finally suboptimal policies. There ‎are works that try to resolve the mentioned problems using combining RL ‎methods, but most of them were applied in the game environments, or ‎they just focused on combining DQN variants. This paper for the first time ‎presents a new method that combines actor-critic and double DQN named ‎Double Actor-Critic (DAC), in the dialogue system, which significantly ‎improves the stability, speed, and performance of dialogue policy learning. ‎ In the actor critic to overcome the slow learning of normal DQN, ‎the critic unit approximates the value function and evaluates the quality ‎of the policy used by the actor, which means that the actor can learn the ‎policy faster. Moreover, to overcome the overestimation issue of DQN, ‎double DQN is employed. Finally, to have a smoother update, a heuristic ‎loss is introduced that chooses the minimum loss of actor-critic and ‎double DQN. ‎ Experiments in a movie ticket booking task show that the ‎proposed method has more stable learning without drop after ‎overestimation and can reach the threshold of learning in fewer episodes ‎of learning. ‎ Unlike previous works that mostly focused on just proposing ‎a combination of DQN variants, this study combines DQN variants with ‎actor-critic to benefit from both policy-based and value-based RL methods ‎and overcome two main issues of both of them, slow learning and ‎overestimation. Experimental results show that the proposed method can ‎make a more accurate conversation with a user as a dialogue policy ‎learner.‎