Summary of Differentiable Tree Search Network, by Dixant Mittal and Wee Sun Lee
Differentiable Tree Search Network
by Dixant Mittal, Wee Sun Lee
First submitted to arxiv on: 22 Jan 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 The paper introduces Differentiable Tree Search Network (D-TSN), a novel neural network architecture for decision-making problems with limited training data. The approach learns a world model from the limited data and determines actions through online search, addressing suboptimal performance of policy functions approximated using deep neural networks. D-TSN strengthens the inductive bias by embedding the algorithmic structure of best-first online search, jointly optimizing the world model and search algorithm to learn a robust world model and mitigate prediction inaccuracies. The architecture also addresses the issue of discontinuous loss function by adopting a stochastic tree expansion policy and introducing an effective variance reduction technique for gradient computation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists created a new way to help machines make decisions when they have very little information to work with. They used a special kind of computer network called D-TSN that helps the machine learn about its world and decide what to do next. This is important because often, these machines don’t do very well when making decisions on their own. The new approach makes sure the machine learns accurately and doesn’t make mistakes. |
Keywords
* Artificial intelligence * Embedding * Loss function * Neural network
