Summary of Reinforcement Learning with Lookahead Information, by Nadav Merlis
Reinforcement Learning with Lookahead Information
by Nadav Merlis
First submitted to arxiv on: 4 Jun 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 proposed reinforcement learning (RL) methods incorporate lookahead information, which is available in many applications such as transactions and navigation. When the environment is known, previous work shows that this lookahead information can drastically increase the collected reward. However, existing approaches for interacting with unknown environments are not well-adapted to these observations. The designed provably-efficient learning algorithms able to incorporate lookahead information achieve linearly increasing the amount of collected reward compared to agents that cannot handle lookahead information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) is a way that machines learn from experience. In some cases, like making transactions or navigating, we have extra information about what will happen if we choose different actions. This helps us make better decisions and get more rewards. Previous studies showed that using this extra information can be very helpful when the environment is known. But in unknown environments, current methods aren’t good at using this information. The researchers developed new algorithms to close this gap and use the lookahead information efficiently. Their approach uses planning based on observed reward and transition distributions instead of just estimated expectations. This leads to achieving regret compared to a baseline that also has access to lookahead information. |
Keywords
» Artificial intelligence » Reinforcement learning
