29 citations · 68 across the 12 of their papers we have counts for
4 papers · 1 filter
Efficient Dialog Policy Learning via Positive Memory Retention
Rui Zhao, Volker Tresp
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typic…
Energy-Based Hindsight Experience Prioritization
Rui Zhao, Volker Tresp
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was…
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Rui Zhao, Volker Tresp
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end…
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong +4
Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models…