论文标题
深入的增强学习的放置优化
Placement Optimization with Deep Reinforcement Learning
论文作者
论文摘要
放置优化是系统和芯片设计中的重要问题，该问题包括将图的节点映射到有限的资源集中，以优化目标，但要受到约束。在本文中，我们首先要激励加强学习作为解决位置问题的解决方案。然后，我们概述了深入的学习是什么。接下来，我们将放置问题作为加强学习问题，并展示如何通过策略梯度优化解决此问题。最后，我们描述了从培训各种位置优化问题的深入强化学习政策中学到的经验教训。
Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints. In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is. We next formulate the placement problem as a reinforcement learning problem and show how this problem can be solved with policy gradient optimization. Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.
