Reproduce “A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations” on OpenResearch
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About this paper
Timothy Flavin, Sandip Sen
Researchers developed the Hide-And-Seek-Engine (HASE), a C++ Dec-POMDP simulator designed for multi-agent operations, achieving up to 33,000,000 environment steps per second and significantly reducing the computational bottleneck for reinforcement learning training. The engine incorporates Data-Oriented Design and zero-copy memory management to facilitate efficient training of cooperative multi-agent policies.
Key insights
- Data-Oriented Design, including 256-byte cache-alignment and thread-local intermediate buffers, significantly enhances simulation throughput in multi-agent environments.
- OS-managed CPU scheduling, which utilizes all logical threads, proved more effective than strict thread pinning for maximizing performance in multi-threaded C++ environments.
- Resolving thread contention between parallel C++ execution and PyTorch by restricting PyTorch threading and using OpenMP passive wait policies is essential for optimal system throughput.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.