Papers Task-Completion Dialogue Policy Learning

“Task-Completion Dialogue Policy Learning” 태그가 달린 논문 6편 · 필터 해제

Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network

2020-11-01 · EMNLP 2020 11 · Sihan Wang, Kaijie Zhou, Kunfeng Lai, Jianping Shen

We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning. Different from the previous deep model-based reinforcement learning methods, whic…

Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)Task-Completion Dialogue Policy Learning

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

2018-11-19 · Yuexin Wu, Xiujun Li, Jingjing Liu, Jianfeng Gao 외

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectivel…

Active Learning Q-Learning Reinforcement Learning Task-Completion Dialogue Policy Learning

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

2018-08-28 · EMNLP 2018 10 · Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu 외

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 fo…

Task-Completion Dialogue Policy Learning

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

2018-01-18 · ACL 2018 7 · Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu 외

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usua…

Reinforcement Learning Reinforcement Learning (RL)Task-Completion Dialogue Policy Learning

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

2017-10-31 · Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu 외

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generati…

Task-Completion Dialogue Policy Learning

Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

2017-04-10 · EMNLP 2017 9 · Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao 외

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and b…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1