Summary of Reward Machines For Deep Rl in Noisy and Uncertain Environments, by Andrew C. Li et al.
Reward Machines for Deep RL in Noisy and Uncertain Environments
by Andrew C. Li, Zizhao Chen, Toryn Q. Klassen, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces Reward Machines, an automaton-inspired framework for specifying instructions, safety constraints, and temporally extended reward-worthy behavior. This approach enables decomposition of RL tasks, leading to improved sample efficiency. While similar formal specifications have been applied to sequential decision-making problems, they rely on ground-truth interpretations of domain-specific vocabulary, which are challenging in real-world scenarios due to partial observability and noisy sensing. The paper explores the use of Reward Machines for Deep RL in noisy and uncertain environments, characterizing the problem as a POMDP and proposing RL algorithms that exploit task structure under uncertain interpretation of the vocabulary. Through theory and experiments, the authors expose pitfalls in naive approaches and demonstrate successful leverage of task structure under noisy interpretations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reward Machines are a new way to help robots and computers learn from experience. This system helps machines make better decisions by breaking down complex tasks into smaller steps. It’s like following a recipe! But, this approach needs something called “ground-truth” information that tells it what the robot or computer should be doing. In real life, this information is often missing because sensors and cameras can be faulty or unclear. The paper looks at how Reward Machines can help deep learning in these situations, where there’s noise and uncertainty. It proposes new ways for machines to learn from experience and make good decisions even when things are not perfect. |
Keywords
» Artificial intelligence » Deep learning
