Summary of How Does Inverse Rl Scale to Large State Spaces? a Provably Efficient Approach, by Filippo Lazzati et al.
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
by Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
First submitted to arxiv on: 6 Jun 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 This paper addresses online Inverse Reinforcement Learning (IRL) in Linear Markov Decision Processes (MDPs). Current algorithms struggle with large state spaces, leading to inefficiencies. The authors introduce the concept of rewards compatibility, which generalizes the feasible reward set. They develop CATY-IRL, a sample-efficient algorithm that scales with the number of states. In tabular settings, CATY-IRL is minimax optimal up to logarithmic factors. Additionally, Reward-Free Exploration (RFE) achieves similar performance, improving upon the state-of-the-art lower bound. The paper presents a unifying framework for IRL and RFE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online learning can help improve our understanding of environments by identifying reward functions. This is achieved through Inverse Reinforcement Learning (IRL), which is often used in online settings. However, current methods struggle to scale when dealing with large state spaces. To address this issue, researchers have focused on estimating the entire set of rewards that explain demonstrations, called the feasible reward set. A new approach called rewards compatibility generalizes the concept of feasible sets and helps improve efficiency. This is achieved through a sample-efficient algorithm called CATY-IRL. |
Keywords
» Artificial intelligence » Online learning » Reinforcement learning
