# Placement Optimization with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.08445 · HTML: https://mlsanity.com/paper/2003.08445/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots](https://mlsanity.com/paper/2205.05748/) (2022)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [AI/ML Algorithms and Applications in VLSI Design and Technology](https://mlsanity.com/paper/2202.10015/) (2022)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [VeRLPy: Python Library for Verification of Digital Designs with Reinforcement Learning](https://mlsanity.com/paper/2108.03978/) (2021)
- [Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing](https://mlsanity.com/paper/2203.07998/) (2022)
- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)

## Abstract (arXiv)

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.