Summary of Imitation Learning in Discounted Linear Mdps Without Exploration Assumptions, by Luca Viano and Stratis Skoulakis and Volkan Cevher
Imitation Learning in Discounted Linear MDPs without exploration assumptions
by Luca Viano, Stratis Skoulakis, Volkan Cevher
First submitted to arxiv on: 3 May 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 new algorithm, ILARL, significantly improves imitation learning in infinite horizon linear Markov Decision Processes (MDPs) by reducing the number of required trajectories. Unlike previous works, ILARL removes exploration assumptions and achieves a bound on sampled trajectories that depends only polynomially on the desired accuracy ε. This is achieved through a connection between imitation learning and online learning in MDPs with adversarial losses. The algorithm also provides improved results for finite horizon cases, achieving an upper bound of O(ε^(-2)). Numerical experiments demonstrate ILARL’s performance superiority over commonly used algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn by copying has been developed, called ILARL. This helps machines make decisions in a world that never ends, like a game where the goal is always moving forward. Previously, it was hard to explore and find good moves without knowing what the goal was. But now, with ILARL, we can do better and only need to try a few things before we get really close to what’s wanted. This new method also works well in situations where time is limited. The results show that this way of learning is better than some other methods people have tried. |
Keywords
» Artificial intelligence » Online learning
