By how much do they improve upon supervised traning methods?
A2C and REINFORCE-joint for joint policy optimization achieve the improvement over SL baseline of 29.4% and  25.7 susses rate,  1.21  AND 1.28 AvgRevard and  0.25 and  -1.34 AvgSucccess Turn Size, respectively.
In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in learning dialog policy with RL include letting a dialog agent to learn against a user simulator. Building a reliable user simulator, however, is not trivial, often as difficult as building a good dialog agent. We address this challenge by jointly optimizing the dialog agent and the user simulator with deep RL by simulating dialogs between the two agents. We first bootstrap a basic dialog agent and a basic user simulator by learning directly from dialog corpora with supervised training. We then improve them further by letting the two agents to conduct task-oriented dialogs and iteratively optimizing their policies with deep RL. Both the dialog agent and the user simulator are designed with neural network models that can be trained end-to-end. Our experiment results show that the proposed method leads to promising improvements on task success rate and total task reward comparing to supervised training and single-agent RL training baseline models.
Task-oriented dialog system is playing an increasingly important role in enabling human-computer interactions via natural spoken language. Different from chatbot type of conversational agents BIBREF0 , BIBREF1 , BIBREF2 , task-oriented dialog systems assist users to complete everyday tasks, which usually involves aggregating information from external resources and planning over multiple dialog turns. Conventional task-oriented dialog systems are designed with complex pipelines BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , and there are usually separately developed modules for natural language understanding (NLU) BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , dialog state tracking (DST) BIBREF11 , BIBREF12 , and dialog management (DM) BIBREF13 , BIBREF14 , BIBREF15 . Such pipeline approach inherently make it hard to scale a dialog system to new domains, as each of these modules has to be redesigned separately with domain expertise. Moreover, credit assignment in such pipeline systems can be challenging, as errors made in upper stream modules may propagate and be amplified in downstream components. Recent efforts have been made in designing end-to-end frameworks for task-oriented dialogs. Wen et al. BIBREF16 and Liu et al. BIBREF17 proposed supervised learning (SL) based end-to-end trainable neural network models. Zhao and Eskenazi BIBREF18 and Li et al. BIBREF19 introduced end-to-end trainable systems using deep reinforcement learning (RL) for dialog policy optimization. Comparing to SL based models, systems trained with RL by exploring the space of possible strategies showed improved model robustness against diverse dialog situations. In RL based dialog policy learning, ideally the agent should be deployed to interact with users and get rewards from real user feedback. However, many dialog samples may be required for RL policy shaping, making it impractical to learn from real users directly from the beginning. Therefore, a user simulator BIBREF20 , BIBREF21 , BIBREF22 is usually used to train the dialog agent to a sufficient starting level before deploying it to the real environment. Quality of such user simulator has a direct impact on the effectiveness of dialog policy learning. Designing a reliable user simulator, however, is not trivial, often as difficult as building a good dialog agent. User simulators used in most of the recent RL based dialog models BIBREF18 , BIBREF22 , BIBREF19 are designed with expert knowledge and complex rules. To address the challenge of lacking a reliable user simulator for dialog agent policy learning, we propose a method in jointly optimizing the dialog agent policy and the user simulator policy with deep RL. We first bootstrap a basic neural dialog agent and a basic neural user simulator by learning directly from dialog corpora with supervised training. We then improve them further by simulating task-oriented dialogs between the two agents and iteratively optimizing their dialog policies with deep RL. The intuition is that we model task-oriented dialog as a goal fulfilling task, in which we let the dialog agent and the user simulator to positively collaborate to achieve the goal. The user simulator is given a goal to complete, and it is expected to demonstrate coherent but diverse user behavior. The dialog agent attempts to estimate the user's goal and fulfill his request by conducting meaningful conversations. Both the two agents aim to learn to collaborate with each other to complete the task but without exploiting the game. Our contribution in this work is two-fold. Firstly, we propose an iterative dialog policy learning method that jointly optimizes the dialog agent and the user simulator in end-to-end trainable neural dialog systems. Secondly, we design a novel neural network based user simulator for task-oriented dialogs that can be trained in a data-driven manner without requiring the design of complex rules. The remainder of the paper is organized as follows. In section 2, we discuss related work on end-to-end trainable task-oriented dialog systems and RL policy learning methods. In section 3, we describe the proposed framework and learning methods in detail. In Section 4, we discuss the experiment setup and analyze the results. Section 5 gives the conclusions.