Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting. In this work, we reformulate I2IT as a step-wise decision-making problem via deep reinforcement learning (DRL) and propose a novel framework that performs RL-based I2IT (RL-I2IT). The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image. Considering that it is challenging to handle high dimensional continuous state and action spaces in the conventional RL framework, we introduce meta policy with a new concept Plan to the standard Actor-Critic model, which is of a lower dimension than the original image and can facilitate the actor to generate a tractable high dimensional action. In the RL-I2IT framework, we also employ a task-specific auxiliary learning strategy to stabilize the training process and improve the performance of the corresponding task. Experiments on several I2IT tasks demonstrate the effectiveness and robustness of the proposed method when facing high-dimensional continuous action space problems.

翻译：大多数现有的图像到图像翻译（I2IT）方法通过单次运行深度学习（DL）模型来生成图像。然而，设计此类单步模型始终具有挑战性，需要大量参数，且容易陷入不良全局极小值和过拟合。在本工作中，我们将I2IT重新表述为基于深度强化学习（DRL）的逐步决策问题，并提出了一种基于强化学习的I2IT（RL-I2IT）新型框架。RL-I2IT框架的关键特征在于将整体学习过程分解为使用轻量级模型的小步骤，从而逐步将源图像连续转换为目标图像。考虑到传统强化学习框架难以处理高维连续状态和动作空间，我们在标准Actor-Critic模型中引入了元策略及新概念"规划"（Plan），该策略维度低于原始图像，可辅助Actor生成可处理的高维动作。在RL-I2IT框架中，我们还采用了任务特定辅助学习策略来稳定训练过程并提升对应任务性能。在多个I2IT任务上的实验证明了所提方法在面对高维连续动作空间问题时的有效性和鲁棒性。