Summary of Efficient Reinforcement Learning in Probabilistic Reward Machines, by Xiaofeng Lin et al.
Efficient Reinforcement Learning in Probabilistic Reward Machines
by Xiaofeng Lin, Xuezhou Zhang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper investigates reinforcement learning in Markov Decision Processes (MDPs) with Probabilistic Reward Machines (PRMs), a type of non-Markovian reward commonly found in robotics tasks. The authors design an algorithm that achieves a regret bound of ( + H2O2A^{3/2} + H), improving upon the best-known bound for MDPs with Deterministic Reward Machines (DRMs). When T H3O3A^2 and OA H, the regret bound leads to a regret of (), matching the established lower bound for MDPs with DRMs up to a logarithmic factor. The paper presents a new simulation lemma for non-Markovian rewards, enabling reward-free exploration given access to an approximate planner. Extensive experiment evaluations demonstrate that the algorithm outperforms prior methods in various PRM environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a type of machine learning called reinforcement learning, which helps robots make good decisions. The researchers created a new way for robots to learn from rewards that might not be entirely predictable. They showed that their method is better than existing ones and can even explore without knowing the exact reward rules. The authors tested their approach in different scenarios and found it outperformed other methods. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
