Summary of Fast Convergence Of Softmax Policy Mirror Ascent, by Reza Asad et al.
Fast Convergence of Softmax Policy Mirror Ascent
by Reza Asad, Reza Babanezhad, Issam Laradji, Nicolas Le Roux, Sharan Vaswani
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 abstract presents a refined policy gradient method called Softmax Policy Mirror Ascent (SPMA), which improves upon previous work by Vaswani et al. [2021] and removes the need for normalization across actions. SPMA achieves linear convergence to the optimal value function, outperforming constant-step-size NPG in tabular MDPs. To handle large state-action spaces, the algorithm uses a log-linear policy parameterization. When extended to the linear function approximation (FA) setting, SPMA requires only solving convex softmax classification problems, unlike MDPO, which is a practical generalization of NPG. The authors evaluate SPMA’s performance on MuJoCo and Atari benchmarks, demonstrating consistent or better performance compared to PPO, TRPO, and MDPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPMA is a new policy gradient method that helps computers learn from mistakes. It’s an improvement over previous methods and can be used in situations with many possible actions and states. The algorithm works by adjusting the probabilities of different actions based on how well they perform. SPMA is able to learn quickly and accurately, making it useful for training artificial intelligence models. |
Keywords
* Artificial intelligence * Classification * Generalization * Softmax
