Summary of The Limits Of Transfer Reinforcement Learning with Latent Low-rank Structure, by Tyler Sam et al.
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
by Tyler Sam, Yudong Chen, Christina Lee Yu
First submitted to arxiv on: 28 Oct 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 The proposed transfer RL algorithm addresses the issue of large state and action spaces in reinforcement learning by leveraging latent low-rank structure. The approach is evaluated across various settings, including Tucker rank structures for transition kernels, and introduces a transfer-ability coefficient alpha that measures the difficulty of representational transfer. The algorithm learns latent representations in source MDPs and exploits linear structure to remove dependencies on state, action, or product spaces, resulting in improved regret bounds. Additionally, information-theoretic lower bounds demonstrate minimax-optimality for most settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new approach to reinforcement learning that can be used in real-world scenarios by reducing the complexity of large problem sizes. The algorithm uses a special type of structure called latent low-rank to learn from one task and apply it to another. This is important because many current algorithms are too slow or expensive to use in practice. The paper shows how this new approach works and why it’s better than existing methods. |
Keywords
* Artificial intelligence * Reinforcement learning
