Summary of Augmented Bayesian Policy Search, by Mahdi Kallel et al.
Augmented Bayesian Policy Search
by Mahdi Kallel, Debabrota Basu, Riad Akrour, Carlo D’Eramo
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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: Deterministic policies are often preferred in physical systems due to their ability to prevent erratic behavior and ease implementation. However, stochastic policies are still essential for exploration. This paper proposes a novel approach, Augmented Bayesian Search (ABS), which combines the benefits of Bayesian Optimization (BO) methods with policy gradient methods. ABS leverages the performance difference lemma to introduce a mean function that enhances the posterior gradient with the deterministic policy gradient. This results in an algorithm that balances the convenience of direct policy search with the scalability of reinforcement learning. The proposed method is validated on high-dimensional locomotion problems, demonstrating competitive performance compared to existing direct policy search schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have been trying to find a better way to make decisions when we don’t know exactly how things will turn out. One type of decision-making is called “deterministic” because it always follows the same rules. This can be good in some situations, but sometimes we need to explore and try new things. This paper introduces a new approach that combines two existing methods to make decisions. It’s called Augmented Bayesian Search (ABS). ABS uses a special formula to make better predictions about how different choices will turn out. The authors tested this method on some complex problems and found it worked well. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
