Summary of When Do Off-policy and On-policy Policy Gradient Methods Align?, by Davide Mambelli et al.
When Do Off-Policy and On-Policy Policy Gradient Methods Align?
by Davide Mambelli, Stephan Bongers, Onno Zoeter, Matthijs T.J. Spaan, Frans A. Oliehoek
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 explores the limitations of policy gradient methods in reinforcement learning, particularly their sample inefficiency. These methods have achieved success in various domains but are typically only used where fast and accurate simulations are available. To improve sample efficiency, researchers often modify the objective function to be computable from off-policy samples without importance sampling. The study focuses on the excursion objective, a well-established off-policy method, and examines the difference between this approach and the traditional on-policy objective, referred to as the on-off gap. The authors provide theoretical analysis showing conditions to reduce the on-off gap and empirical evidence of shortfalls when these conditions are not met. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Policy gradient methods in reinforcement learning can be slow because they need many samples to learn. These methods work well in some areas but are often used where fast simulations are available. To make them faster, researchers change the goal function so it can use off-policy data without special weighting. The paper looks at how this changes the outcome and finds that there’s a gap between on-policy and off-policy goals. The authors show what makes the gap smaller and give examples of when it’s not. |
Keywords
* Artificial intelligence * Objective function * Reinforcement learning
