Summary of Blendrl: a Framework For Merging Symbolic and Neural Policy Learning, by Hikaru Shindo et al.
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
by Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel reinforcement learning (RL) framework called BlendRL is proposed, which seamlessly integrates symbolic reasoning and intuitive reactions within RL agents. This approach addresses the limitations of traditional RL methods, which either rely on opaque neural networks or predefined symbols and rules. BlendRL combines logic and neural policies using mixtures, allowing agents to leverage both paradigms’ strengths. The framework’s effectiveness is demonstrated through experiments in standard Atari environments, where BlendRL agents outperform both neural and symbolic baselines, showcasing robustness to environmental changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BlendRL is a new way for machines to learn by combining two types of thinking: logical reasoning and instinctive reactions. Right now, most machine learning systems can only do one or the other. This makes them limited in what they can accomplish. The BlendRL system fixes this problem by letting agents use both types of thinking at the same time. This means they can make smart decisions based on rules, while also reacting quickly to changing situations. The authors tested BlendRL in video game-like environments and found that it outperformed other methods. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
