Summary of Finding Path and Cycle Counting Formulae in Graphs with Deep Reinforcement Learning, by Jason Piquenot et al.
Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning
by Jason Piquenot, Maxime Bérar, Pierre Héroux, Jean-Yves Ramel, Romain Raveaux, Sébastien Adam
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Formal Languages and Automata Theory (cs.FL)
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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 A reinforcement learning algorithm called Grammar Reinforcement Learning (GRL) combines Monte Carlo Tree Search (MCTS) with a transformer architecture that models a Pushdown Automaton (PDA) within a context-free grammar (CFG) framework. GRL addresses the problem of efficiently counting paths and cycles in graphs, which is crucial for network analysis, computer science, biology, and social sciences. The algorithm discovers novel matrix-based formulas for path/cycle counting that improve computational efficiency by factors of two to six compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GRL uses a special kind of learning called reinforcement learning. It helps us count paths and cycles in graphs more efficiently. Graphs are like maps, but instead of roads, they have lines and shapes. We use computers to analyze these graphs, and GRL makes it faster. It does this by finding new formulas that work better than old ones. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
