mlsanityData to 14 Oct 2026

Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement

arXiv 2610.20224 ↗First posted 14 Oct 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations also fits: RL for games and combinatorial optimization

Abstract (arXiv)

Graph Neural Networks (GNNs) and Reinforcement Learning (RL) have recently been explored for VLSI placement optimization, but their effectiveness for post-legalization placement refinement remains unclear. This paper presents a systematic design space exploration of a GNN-Proximal Policy Optimization (GNN-PPO) framework across three ISPD2015 benchmarks. We evaluate 18 configurations covering reward functions, GNN architectures, action spaces, candidate-set sizes, and training strategies. We analyse the results we obtain to identify pitfalls and to assess the prospects and opportunities of the RL methods for post-legalization placement. On top of that we propose a design classification approach to guide the design decisions of RL models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 14 references that are in the map. First posted on arXiv 14 Oct 2026.

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2 of 14 references.

References · 2

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320

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