Summary of Upside-down Reinforcement Learning For More Interpretable Optimal Control, by Juan Cardenas-cartagena et al.
Upside-Down Reinforcement Learning for More Interpretable Optimal Control
by Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich, Matthia Sabatelli
First submitted to arxiv on: 18 Nov 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 proposes Upside-Down Reinforcement Learning (UDRL), a novel paradigm that learns to predict actions from states and desired commands. Unlike traditional Model-Free RL algorithms, which learn to map states to expected rewards or search for policies that maximize a performance function, UDRL is formulated as a Supervised Learning problem and has been successfully tackled by Neural Networks (NNs). The authors investigate whether other function approximation algorithms, such as tree-based methods like Random Forests and Extremely Randomized Trees, can also be used within the UDRF framework. Experiments performed on popular optimal control benchmarks show that these alternative algorithms can perform similarly to NNs, with the added benefit of producing more interpretable policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do Reinforcement Learning (RL) called Upside-Down RL. In traditional RL, you try to figure out what actions will get you the best reward. But in Upside-Down RL, you’re trying to predict what action someone else wants you to take, given the situation and what they want. This is a different approach that has been successful using special kinds of computers called Neural Networks (NNs). The researchers wanted to see if other types of algorithms could also be used for this type of learning. They tested some new methods on some popular benchmark problems and found that they worked just as well as the NNs, with the added benefit of making it easier to understand why the computer is choosing certain actions. |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised
