Summary of Compositional Automata Embeddings For Goal-conditioned Reinforcement Learning, by Beyazit Yalcinkaya et al.
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
by Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The proposed approach represents temporal goals using compositions of deterministic finite automata (cDFAs) to guide reinforcement learning (RL) agents. This balances formal semantics with ease of interpretation, allowing cDFAs to be understood by anyone familiar with flowcharts. However, this infinity of concepts and subtle changes can result in drastically different tasks, making conditioning agent behavior challenging. To address this, a graph neural network is pre-trained on “reach-avoid derived” DFAs, enabling zero-shot generalization to various task classes and accelerated policy specialization without hierarchical methods’ suboptimality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to control AI agents’ behavior is proposed by using compositions of deterministic finite automata (cDFAs) as goal representations. This makes it easier for people to understand what the AI wants to achieve, but there are many possible goals and small changes can make a big difference. To solve this problem, the pre-training of graph neural networks on certain types of cDFAs is proposed, which allows the AI agent to learn quickly without getting stuck. |
Keywords
» Artificial intelligence » Generalization » Graph neural network » Reinforcement learning » Semantics » Zero shot
