Summary of Unveiling Options with Neural Decomposition, by Mahdi Alikhasi and Levi H. S. Lelis
Unveiling Options with Neural Decomposition
by Mahdi Alikhasi, Levi H. S. Lelis
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel reinforcement learning algorithm is proposed to enable agents to generalize knowledge from specific tasks to related ones. The approach decomposes neural networks into reusable sub-policies, which are then used to synthesize temporally extended actions, or options. Neural networks with piecewise linear activation functions are employed, allowing them to be mapped to an equivalent tree similar to oblique decision trees. Each sub-tree represents a sub-policy of the main policy, which is converted into options by wrapping it with while-loops of varied iteration numbers. To handle the large number of options, a selection mechanism based on minimizing the Levin loss for a uniform policy is proposed. Empirical results in two grid-world domains demonstrate that the method can identify useful options, accelerating learning on similar but different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers learn from experience is introduced. This approach helps them remember what they’ve learned about one task and use it to solve similar problems. The algorithm breaks down complex neural networks into smaller pieces, called sub-policies, that can be used again and again. These sub-policies are then combined with loops to create longer-term actions, or options. To make sense of all these options, the algorithm proposes a new way to pick the best ones based on how well they work together. The results show that this approach helps computers learn faster when faced with similar but different challenges. |
Keywords
* Artificial intelligence * Reinforcement learning
