2.6k citations · 6.5k across the 69 of their papers we have counts for
9 papers · 1 filter
Boosting the Actor with Dual Critic
Bo Dai, Albert Shaw, Niao He +2
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimalit…
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li +5
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The…
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao +3
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawi…
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li +4
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,…
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Xiujun Li, Yun-Nung Chen, Lihong Li +2
Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performanc…
End-to-End Task-Completion Neural Dialogue Systems
Xiujun Li, Yun-Nung Chen, Lihong Li +2
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream m…