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Bbq-networks: Efficient Exploration In Deep Reinforcement Learning For Task-oriented Dialogue Systems

Zachary C. Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng . Arxiv 2016 – 105 citations

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Compositional Generalization Dialogue & Multi Turn Efficiency Productivity Enhancement Reinforcement Learning

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 (\epsilon)-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.

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