A reinforcement-learning-trained policy for selecting nonlocal cluster moves improves a Monte Carlo solver for hard 4-SAT benchmarks over simulated annealing.
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
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abstract
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX. PGMax supports general factor graphs with tractable factors, and leverages modern accelerators like GPUs for inference. Compared with existing alternatives, PGMax obtains higher-quality inference results with up to three orders-of-magnitude inference time speedups. PGMax additionally interacts seamlessly with the rapidly growing JAX ecosystem, opening up new research possibilities. Our source code, examples and documentation are available at https://github.com/deepmind/PGMax.
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2025 1verdicts
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Nonlocal Monte Carlo via Reinforcement Learning
A reinforcement-learning-trained policy for selecting nonlocal cluster moves improves a Monte Carlo solver for hard 4-SAT benchmarks over simulated annealing.