Summary of Learning in Complex Action Spaces Without Policy Gradients, by Arash Tavakoli et al.
Learning in complex action spaces without policy gradients
by Arash Tavakoli, Sina Ghiassian, Nemanja Rakićević
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: This paper challenges conventional wisdom by exploring the applicability of policy gradient and action-value methods in complex action spaces. While previous studies have shown equivalences between these paradigms in small, finite action spaces, their computational performance and applicability diverge as the complexity of the action space increases. The authors hypothesize that this disparity stems not from inherent properties of policy gradients but from universal principles that can be applied to action-value methods. They identify three such principles and propose a framework for incorporating them into action-value methods, instantiated in QMLE (Q-learning with maximum likelihood estimation). The results demonstrate that QMLE can be used in complex action spaces with a controllable computational cost comparable to policy gradient methods, without relying on policy gradients. Moreover, QMLE shows strong performance on the DeepMind Control Suite, matching state-of-the-art methods like DMPO and D4PG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper questions why certain algorithms work better in complex situations than others. It looks at two types of algorithms (policy gradient and action-value) that are used to make decisions in computer simulations. Researchers previously found that these algorithms performed similarly when the options were simple, but their performance diverged as the complexity increased. The authors propose a new approach that can be applied to both types of algorithms, which they call QMLE. This approach achieves similar results without using one type of algorithm over the other. They tested this approach on complex simulations and found it performed well, matching state-of-the-art methods. |
Keywords
* Artificial intelligence * Likelihood
