Poster
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Nan Ding · Radu Soricut
Pacific Ballroom #109
Keywords: [ Reinforcement Learning ] [ Natural Language Processing ] [ Attention Models ] [ Recurrent Networks ]
Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity of maximum-likelihood approaches. We apply this new cold-start reinforcement learning method in training sequence generation models for structured output prediction problems. Empirical evidence validates this method on automatic summarization and image captioning tasks.
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