title: Batch Policy Gradient  Methods for  Improving Neural Conversation Models
abstract: We study reinforcement learning of chat-bots with recurrent neural network
  architectures when the rewards are noisy and expensive to
  obtain. For instance, a chat-bot used in automated customer service support can
  be scored by quality assurance agents, but this process can be expensive, time consuming
  and noisy. 
  Previous reinforcement learning work for natural language uses on-policy updates
  and/or is designed for on-line learning settings.
  We demonstrate empirically that such strategies are not appropriate for this setting
  and develop an off-policy batch policy gradient method (\bpg).
  We demonstrate the efficacy of our method via a series of
  synthetic experiments and an Amazon Mechanical Turk experiment on
  a restaurant recommendations dataset.
  
  
authors: Kirthevasan Kandasamy Yoram Bachrach Ryota Tomioka Daniel Tarlow David Carter
hIndex: Yoram Bachrach 35