Summary of Speeding Up Policy Simulation in Supply Chain Rl, by Vivek Farias et al.
Speeding up Policy Simulation in Supply Chain RL
by Vivek Farias, Joren Gijsbrechts, Aryan Khojandi, Tianyi Peng, Andrew Zheng
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle a bottleneck in policy optimization algorithms that simulates a single trajectory of a dynamical system under a state-dependent policy. The authors propose an iterative algorithm called Picard Iteration to accelerate policy simulation, which assigns tasks to independent processes and updates a cache at the end of each iteration. This scheme is implemented on GPUs, allowing for batched evaluation of the policy across a single trajectory. The researchers prove that their method converges quickly in many supply chain optimization problems and demonstrate practical speedups of 400x on large-scale SCO problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Picard Iteration algorithm helps solve a problem in optimizing policies by speeding up simulations of complex systems. By breaking down big tasks into smaller ones and using computers to do lots of calculations at the same time, this method makes it much faster to test different options for managing supply chains or other complex systems. |
Keywords
» Artificial intelligence » Optimization
