Summary of Monitored Markov Decision Processes, by Simone Parisi et al.
Monitored Markov Decision Processes
by Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel framework for reinforcement learning (RL) called Monitored MDPs, where agents cannot always observe rewards in response to their actions. The authors formalize this setting, discussing its theoretical and practical implications, and propose algorithms to tackle it. This framework encompasses both new and existing problems, laying the groundwork for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where robots or computers learn from experience by interacting with their environment. Typically, they get feedback in the form of rewards or punishments for their actions. But what if this feedback isn’t always available? For example, what if humans need to supervise the robot’s actions or special equipment needs to be activated to receive feedback? This paper introduces a new way of thinking about reinforcement learning that allows agents to learn even when they can’t always see the rewards. |
Keywords
* Artificial intelligence * Reinforcement learning
