Proximal Policy Optimization and its Dynamic Version for Sequence Generation

24 Aug 2018 • Yi-Lin Tuan • Jinzhi Zhang • Yujia Li • Hung-Yi Lee

In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in adversarial learning. In this paper, we replace policy gradient with proximal policy optimization (PPO), which is a proved more efficient reinforcement learning algorithm, and propose a dynamic approach for PPO (PPO-dynamic)... (read more)

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