Summary of Value Improved Actor Critic Algorithms, by Yaniv Oren et al.
Value Improved Actor Critic Algorithms
by Yaniv Oren, Moritz A. Zanger, Pascal R. van der Vaart, Mustafa Mert Celikok, Matthijs T. J. Spaan, Wendelin Bohmer
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed approach extends the standard framework of Actor Critic algorithms by incorporating value-improvement, a second greedification operator that updates the policy’s value estimate. This allows for greedier updates while maintaining the steady gradient-based improvement to the parameterized acting policy. The approach converges in the Generalized Policy Iteration scheme and empirically improves or matches performance over baselines in various environments from the DeepMind continuous control domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To learn optimal actions, Actor Critic algorithms use deep Neural Networks (DNNs) to update policies. However, this process has a tradeoff between greedification and stability. To address this, we propose extending the standard framework with value-improvement, allowing for greedier updates while maintaining stable policy improvements. |
