Summary of The Value Of Reward Lookahead in Reinforcement Learning, by Nadav Merlis et al.
The Value of Reward Lookahead in Reinforcement Learning
by Nadav Merlis, Dorian Baudry, Vianney Perchet
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 investigates the value of future reward information in reinforcement learning (RL) when agents interact with changing environments. Traditionally, RL focuses on maximizing expected cumulative rewards by observing rewards after actions. However, in many real-world scenarios, some reward information is available beforehand, such as prices or traffic updates. This study quantifies the benefits of incorporating partial future-reward lookahead in competitive analysis, exploring the ratio between standard RL agents and those with lookahead capabilities. The results reveal surprising connections to offline RL and reward-free exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how much better an agent can do if it knows some rewards ahead of time in a game where you try to get rewards by making good choices. Usually, we only know the rewards after we make our choice. But sometimes we might know a little bit about what will happen before we choose. The study tries to figure out how useful this extra information is and finds that it can be really helpful. |
Keywords
* Artificial intelligence * Reinforcement learning
