Summary of Provably Efficient Exploration in Reward Machines with Low Regret, by Hippolyte Bourel et al.
Provably Efficient Exploration in Reward Machines with Low Regret
by Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Chenxiao Ma, Mohammad Sadegh Talebi
First submitted to arxiv on: 26 Dec 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) approach is presented for decision processes involving non-Markovian rewards, leveraging high-level knowledge in the form of probabilistic reward machines. The average-reward criterion is employed, assessing learning performance through regret. A model-based RL algorithm is proposed that exploits the structure induced by these machines, outperforming existing algorithms. High-probability and non-asymptotic bounds on regret are derived, demonstrating gains over obliviously applied methods. A regret lower bound for the studied setting is also established. This marks a first attempt to tailor and analyze regret specifically for RL with probabilistic reward machines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to learn about a new way that computers can make decisions when they don’t know exactly what will happen next. It’s like trying to decide whether to take a detour on a road trip without knowing the traffic ahead of time. Researchers have developed an algorithm that uses information about the task at hand, in this case, a “reward machine,” to help it make better decisions. This is important because it allows the computer to learn more efficiently and make fewer mistakes. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
