Summary of Q-exponential Family For Policy Optimization, by Lingwei Zhu et al.
q-exponential family for policy optimization
by Lingwei Zhu, Haseeb Shah, Han Wang, Yukie Nagai, Martha White
First submitted to arxiv on: 14 Aug 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 This paper proposes a novel policy optimization method that generalizes the traditional Gaussian parametrization for continuous action spaces. The authors introduce the q-exponential family of policies, which allows for the specification of both heavy-tailed and light-tailed distributions. They investigate the performance of this policy family in various actor-critic algorithms on both online and offline problems. The results show that heavy-tailed policies tend to be more effective and can consistently improve upon Gaussian policies. Specifically, the Student’s t-distribution is found to be more stable than the Gaussian across different settings. The proposed q-Gaussian policy for Tsallis Advantage Weighted Actor-Critic also demonstrates strong performance in offline benchmark problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to make decisions (policies) that can handle big or small changes (heavy-tailed or light-tailed). The authors try out this new approach with different algorithms on both immediate and delayed challenges. They find that these new policies often do better than the usual Gaussian ones. In particular, they discover that a special type of policy called the Student’s t-distribution is more consistent in its performance. |
Keywords
» Artificial intelligence » Optimization
