Summary of Local Linearity: the Key For No-regret Reinforcement Learning in Continuous Mdps, by Davide Maran et al.
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
by Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli
First submitted to arxiv on: 31 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 paper addresses the long-standing challenge of achieving the no-regret property in Reinforcement Learning (RL) problems in continuous state and action-space environments. Existing solutions either rely on specific assumptions or provide bounds that are vacuous in certain regimes. The authors identify local linearity as the key feature that enables Markov Decision Processes (MDPs) to be both learnable and feasible, with regret bounds that scale polynomially with the time horizon H. They introduce a novel MDP representation class, Locally Linearizable MDPs, which generalizes other classes like Linear MDPs and MDPS with low inherent Bellman error. The authors then present Cinderella, a no-regret algorithm for this representation class, and demonstrate that all known learnable and feasible MDP families are representable within this class. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving a big problem in artificial intelligence called Reinforcement Learning. It’s trying to figure out how to make computers learn from experience without making mistakes. The authors found a way to solve this problem by using a special kind of math called local linearity. They created a new way to represent complex problems, which they call Locally Linearizable MDPs. This allows them to create an algorithm that can learn quickly and accurately, even when the problem is very hard. |
Keywords
* Artificial intelligence * Reinforcement learning
