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        <title>Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems</title>
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    <abstract>In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). Although I2A achieves a higher success rate than baselines by augmenting predicted future into a policy network, its complicated architecture introduces unwanted instability. In this work, we propose actor-double-critic (ADC) to improve the stability and overall performance of I2A. ADC simplifies the architecture of I2A to reduce excessive parameters and hyper-parameters. More importantly, a separate model-based critic shares parameters between actions and makes back-propagation explicit. In our experiments on Cambridge Restaurant Booking task, ADC enhances success rates considerably and shows robustness to imperfect environment models. In addition, ADC exhibits the stability and sample-efficiency as significantly reducing the baseline standard deviation of success rates and reaching the 80% success rate with half training data.</abstract>
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%0 Conference Proceedings
%T Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems
%A Wu, Yen-chen
%A Tseng, Bo-Hsiang
%A Gasic, Milica
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wu-etal-2020-actor
%X In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). Although I2A achieves a higher success rate than baselines by augmenting predicted future into a policy network, its complicated architecture introduces unwanted instability. In this work, we propose actor-double-critic (ADC) to improve the stability and overall performance of I2A. ADC simplifies the architecture of I2A to reduce excessive parameters and hyper-parameters. More importantly, a separate model-based critic shares parameters between actions and makes back-propagation explicit. In our experiments on Cambridge Restaurant Booking task, ADC enhances success rates considerably and shows robustness to imperfect environment models. In addition, ADC exhibits the stability and sample-efficiency as significantly reducing the baseline standard deviation of success rates and reaching the 80% success rate with half training data.
%R 10.18653/v1/2020.findings-emnlp.75
%U https://aclanthology.org/2020.findings-emnlp.75
%U https://doi.org/10.18653/v1/2020.findings-emnlp.75
%P 854-863

Markdown (Informal)

[Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems](https://aclanthology.org/2020.findings-emnlp.75) (Wu et al., Findings 2020)

• Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems (Wu et al., Findings 2020)

ACL

• Yen-chen Wu, Bo-Hsiang Tseng, and Milica Gasic. 2020. Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 854–863, Online. Association for Computational Linguistics.