Cold-Start Reinforcement Learning with Softmax Policy Gradient

Nan Ding; Radu Soricut

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arXiv · 1709.09346

Cold-Start Reinforcement Learning with Softmax Policy Gradient

Nan Ding·Radu Soricut

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Abstract

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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Nan Ding, Radu Soricut. 2017-10-13. Cold-Start Reinforcement Learning with Softmax Policy Gradient. https://arxiv.org/abs/1709.09346

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