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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments

February 1, 2023

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Authors

Yangyang Zhao

South China University of Technology


Zhenyu Wang

South China University of Technology


Kai Yin

South China University of Technology


Rui Zhang

South China University of Technology


Zhenhua Huang

South China University of Technology


Pei Wang

South China University of Technology


DOI:

10.1609/aaai.v34i05.6516


Abstract:

Task-oriented dialogue systems provide a convenient interface to help users complete tasks. An important consideration for task-oriented dialogue systems is the ability to against the noise commonly existed in the real-world conversation. Both rule-based strategies and statistical modeling techniques can solve noise problems, but they are costly. In this paper, we propose a new approach, called Dynamic Reward-based Dueling Deep Dyna-Q (DR-D3Q). The DR-D3Q can learn policies in noise robustly, and it is easy to implement by combining dynamic reward and the Dueling Deep Q-Network (Dueling DQN) into Deep Dyna-Q (DDQ) framework. The Dueling DQN can mitigate the negative impact of noise on learning policies, but it is inapplicable to dialogue domain due to different reward mechanisms. Unlike typical dialogue reward function, we integrate dynamic reward that provides reward in real-time for agent to make Dueling DQN adapt to dialogue domain. For the purpose of supplementing the limited amount of real user experiences, we take the DDQ framework as the basic framework. Experiments using simulation and human evaluation show that the DR-D3Q significantly improve the performance of policy learning tasks in noisy environments.1

Topics: AAAI

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Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments Proceedings of the AAAI Conference on Artificial Intelligence (2020) 9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments AAAI 2020, 9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang (2020). Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments. Proceedings of the AAAI Conference on Artificial Intelligence, 9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. 2020. Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments. "Proceedings of the AAAI Conference on Artificial Intelligence". 9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. (2020) "Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments", Proceedings of the AAAI Conference on Artificial Intelligence, p.9676-9684

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang, "Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments", AAAI, p.9676-9684, 2020.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. "Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. "Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 9676-9684.

Yangyang Zhao||Zhenyu Wang||Kai Yin||Rui Zhang||Zhenhua Huang||Pei Wang. Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments. AAAI[Internet]. 2020[cited 2023]; 9676-9684.


ISSN: 2374-3468


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