Summary of A Variance Minimization Approach to Temporal-difference Learning, by Xingguo Chen et al.
A Variance Minimization Approach to Temporal-Difference Learning
by Xingguo Chen, Yu Gong, Shangdong Yang, Wenhao Wang
First submitted to arxiv on: 10 Nov 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 approach to reinforcement learning, this paper presents a variance minimization (VM) method for value-based RL, departing from traditional error minimization strategies. The VM approach is based on two objectives: Variance of Bellman Error (VBE) and Variance of Projected Bellman Error (VPBE), which are used to derive the VMTD, VMTDC, and VMETD algorithms. Convergence proofs and optimal policy invariance are provided for these algorithms, demonstrating their effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a key area of artificial intelligence that helps machines learn from experience. This paper presents a new way to make decisions using this approach, called variance minimization. Instead of trying to get the correct answer every time, this method focuses on reducing uncertainty in the decision-making process. The authors propose three algorithms and show they work well through experiments. |
Keywords
* Artificial intelligence * Reinforcement learning
