Summary of Neural Reward Machines, by Elena Umili et al.
Neural Reward Machines
by Elena Umili, Francesco Argenziano, Roberto Capobianco
First submitted to arxiv on: 16 Aug 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 The paper proposes Neural Reward Machines (NRMs), a novel neurosymbolic framework that combines reinforcement learning with semisupervised symbol grounding. NRMs are designed to reason and learn in non-Markovian domains, where agents need to consider the entire history of state-action pairs. The authors show that NRMs can outperform Deep RL methods by incorporating prior knowledge without requiring a symbol grounding function. This is achieved through the probabilistic relaxation of Moore Machines, which allows for efficient reasoning in non-symbolic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help machines learn and make decisions in complex situations. Right now, most AI systems are only good at solving problems that can be broken down into simple steps. But what if we want them to solve problems that involve remembering lots of information from the past? That’s where Neural Reward Machines come in. These machines use a combination of learning and reasoning to make decisions based on complex patterns. The researchers showed that these machines can do better than other AI systems by using prior knowledge, without needing a special “translation” system. This is important because it could help us build more advanced AI systems that can handle tricky problems. |
Keywords
* Artificial intelligence * Grounding * Reinforcement learning * Translation
