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Greatest papers with code

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

ACL 2018 MiuLab/DDQ

During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience.

TASK-COMPLETION DIALOGUE POLICY LEARNING

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

EMNLP 2018 MiuLab/D3Q

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning.

TASK-COMPLETION DIALOGUE POLICY LEARNING

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

19 Nov 2018CrickWu/Swtich-DDQ

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences.

ACTIVE LEARNING Q-LEARNING TASK-COMPLETION DIALOGUE POLICY LEARNING