Summary of Exploration Is Harder Than Prediction: Cryptographically Separating Reinforcement Learning From Supervised Learning, by Noah Golowich et al.
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
by Noah Golowich, Ankur Moitra, Dhruv Rohatgi
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS)
GrooveSquid.com Paper Summaries
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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 cryptographic separation between reinforcement learning (RL) and supervised learning is established, showcasing a class of block MDPs where reward-free exploration is computationally harder than the associated regression problem. This medium-difficulty summary highlights the paper’s findings on the hardness of RL in certain scenarios, featuring the use of block MDPs and decoding functions. The work also demonstrates the impossibility of efficient RL algorithms for reward-directed learning in these MDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way that computers learn from experience. This type of learning is important because it’s how we can teach computers to make decisions on their own. But what if we didn’t want the computer to get any rewards or feedback? Could they still figure things out? A new study shows that in some cases, this type of learning is actually much harder than a simpler type of learning called supervised learning. |
Keywords
* Artificial intelligence * Regression * Reinforcement learning * Supervised
