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BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

arXiv 1711.05715 ↗First posted 15 Nov 2017cs.AI cs.CL cs.LGcited by 30

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Abstract (arXiv)

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, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as ε-greedy, Boltzmann, bootstrapping, and intrinsic-reward-based ones. Additionally, we show that spiking the replay buffer with experiences from just a few successful episodes can make Q-learning feasible when it might otherwise fail.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 8; 2022: 10; 2023: 7; 2024: 4.

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  1. 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
  2. 2021Uncertainty Weighted Actor-Critic for Offline Reinforcement Learningcited by 126
  3. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 58
  4. 2023SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Promptingcited by 39
  5. 2022A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learningcited by 30
  6. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  7. 2022Learning Deformable Object Manipulation from Expert Demonstrationscited by 19
  8. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  9. 2021Bayesian Bellman Operatorscited by 16
  10. 2022Dynamic Dialogue Policy for Continual Reinforcement Learningcited by 12
  11. 2022Toward Self-learning End-to-End Task-Oriented Dialog Systemscited by 9
  12. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
18 more
  1. 2022Fast online inference for nonlinear contextual bandit based on Generative Adversarial Networkcited by 7
  2. 2024Meta Clustering of Neural Banditscited by 7
  3. 2022Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestrationcited by 6
  4. 2024Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptabilitycited by 6
  5. 2023TOD-Flow: Modeling the Structure of Task-Oriented Dialoguescited by 4
  6. 2024Deep Exploration with PAC-Bayescited by 4
  7. 2021High-Quality Diversification for Task-Oriented Dialogue Systemscited by 3
  8. 2021What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisationcited by 3
  9. 2023Bayesian Exploration Networkscited by 3
  10. 2021Analytically Tractable Bayesian Deep Q-Learningcited by 2
  11. 2022Knowledge-Guided Exploration in Deep Reinforcement Learningcited by 2
  12. 2023Learning How to Infer Partial MDPs for In-Context Adaptation and Explorationcited by 2
  13. 2024Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learningcited by 2
  14. 2023Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimizationcited by 1
  15. 2023Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systemscited by 1
  16. 2023Global Hierarchical Neural Networks using Hierarchical Softmaxcited by 1
  17. 2022Recent Progress in Conversational AIcited by 0
  18. 2022Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddingscited by 0