Summary of Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation, by Fengdi Che et al.
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
by Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A Ramirez, Christopher K Harris, A. Rupam Mahmood, Dale Schuurmans
First submitted to arxiv on: 31 May 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 Medium Difficulty Summary: We demonstrate that combining a target network with over-parameterized linear function approximation enables weaker convergence conditions for bootstrapped value estimation in specific scenarios, even when using off-policy data. Our findings show that this combination is naturally satisfied for expected updates across the entire state-action space or learning from complete episodic Markov decision processes. In contrast, relying solely on a target network or an over-parameterized model does not provide such guarantees. We also extend our results to truncated trajectories, demonstrating convergence for all tasks with minor modifications, similar to value truncation for final states. Our primary result focuses on temporal difference estimation for prediction, providing high-probability value estimation error bounds and empirical analysis on Baird’s counterexample and a Four-room task. Furthermore, we explore the control setting, illustrating that similar convergence conditions apply to Q-learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Scientists have discovered a way to make machine learning algorithms work better even when they’re not getting perfect data. They found that combining two types of models can help the algorithm learn from incomplete information and make more accurate predictions. This breakthrough could be used in many areas, such as robots or self-driving cars, where machines need to make decisions based on incomplete data. |
Keywords
» Artificial intelligence » Machine learning » Probability
