GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement

Excerpt

arXiv:2610.06489v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising approach for placement optimization, particularly when combined with graph neural networks (GNNs) that capture circuit connectivity. However, most learning-based placement approaches focus on floorplanning, macro placement, or global placement, while detailed placement refinement remains relatively unexplored. In this paper, we present GPlaceRL, an open-source graph reinforcement learning frame