PaintBot：一种用于自然媒体绘画的强化学习方法
PaintBot: A Reinforcement Learning Approach for Natural Media Painting
摘要：
我们提出了一个新的自动化数字绘画框架，基于绘画代理通过强化学习训练。为了合成图像，智能体选择一系列连续值动作，这些动作表示原始绘画笔划，这些笔划累积在数字画布上。动作选择是由一个给定的参考图像，代理试图复制的动作空间和代理的学习策略的限制。使用近端策略优化强化学习的变体来确定喷涂代理策略。在训练过程中，我们的代理呈现从参考图像集合中采样的补丁。为了加速训练收敛，我们采用了一种课程学习策略，根据参考补丁使用当前策略的挑战性对其进行采样。我们用不同的损失函数进行实验，包括像素和感知损失，这对学习的策略产生了不同的影响。我们证明了我们的绘画代理可以学习一个有效的政策，一个高维连续的动作空间，包括笔的压力，宽度，倾斜和颜色，为各种绘画风格。通过由粗到细的细化过程，我们的代理可以以所需的风格绘制任意复杂的图像。
英文摘要：
We propose a new automated digital painting framework, based on a painting agent trained through reinforcement learning. To synthesize an image, the agent selects a sequence of continuous-valued actions representing primitive painting strokes, which are accumulated on a digital canvas. Action selection is guided by a given reference image, which the agent attempts to replicate subject to the limitations of the action space and the agent's learned policy. The painting agent policy is determined using a variant of proximal policy optimization reinforcement learning. During training, our agent is presented with patches sampled from an ensemble of reference images. To accelerate training convergence, we adopt a curriculum learning strategy, whereby reference patches are sampled according to how challenging they are using the current policy. We experiment with differing loss functions, including pixel-wise and perceptual loss, which have consequent differing effects on the learned policy. We demonstrate that our painting agent can learn an effective policy with a high dimensional continuous action space comprising pen pressure, width, tilt, and color, for a variety of painting styles. Through a coarse-to-fine refinement process our agent can paint arbitrarily complex images in the desired style.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
