Summary of An Agent Design with Goal Reaching Guarantees For Enhancement Of Learning, by Pavel Osinenko et al.
An agent design with goal reaching guarantees for enhancement of learning
by Pavel Osinenko, Grigory Yaremenko, Georgiy Malaniya, Anton Bolychev, Alexander Gepperth
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY); Dynamical Systems (math.DS)
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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 algorithm aims to efficiently learn a near-optimal policy for solving Markov decision processes, while ensuring a goal-reaching property of a given basis policy. The approach suggests augmenting any agent with a critic and provides a formal proof of the goal-reaching property. Comparative experiments demonstrate that this learning can indeed be boosted while achieving the desired outcome. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an algorithm to help agents learn near-optimal policies quickly, while also reaching specific goals. They showed how their method could work with any agent that has a “critic” component, and provided evidence that it works well in practice. |
