Summary of Behind the Myth Of Exploration in Policy Gradients, by Adrien Bolland et al.
Behind the Myth of Exploration in Policy Gradients
by Adrien Bolland, Gaspard Lambrechts, Damien Ernst
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes a novel analysis framework for policy-gradient algorithms used in reinforcement learning to solve control problems. It highlights two key effects of including exploration terms in the learning objective: smoothing the learning objective and eliminating local optima while preserving the global maximum, and modifying gradient estimates to increase the probability of achieving an optimal policy. The authors also empirically demonstrate these effects using entropy bonuses-based exploration strategies, revealing their limitations and opening up avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers learn to make good decisions when they have to explore new things. It shows that including “exploration” in the learning process helps get rid of bad local solutions and find a better overall solution. This is important because it means computers can learn faster and more accurately. The authors also tested this idea using a specific way to encourage exploration, and found that it works but has some limitations. |
Keywords
* Artificial intelligence * Probability * Reinforcement learning
