Summary of Sample-efficient Constrained Reinforcement Learning with General Parameterization, by Washim Uddin Mondal et al.
Sample-Efficient Constrained Reinforcement Learning with General Parameterization
by Washim Uddin Mondal, Vaneet Aggarwal
First submitted to arxiv on: 17 May 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 paper proposes the Primal-Dual Accelerated Natural Policy Gradient (PD-ANPG) algorithm for constrained Markov Decision Problems (CMDPs). The goal is to maximize rewards while ensuring that costs remain below a threshold. Building on momentum-based acceleration, the PD-ANPG algorithm achieves an global optimality gap and constraint violation with ((1-){-7}{-2}) sample complexity for general parameterized policies. This improves the state-of-the-art in general parameterized CMDPs by a factor of ((1-){-1}{-2}), achieving the theoretical lower bound in ^{-1}. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a special problem where an agent must balance rewards and costs. They create a new way to find the best solution using “momentum-based acceleration”. This helps the algorithm work better, with fewer samples needed. The result is faster and more efficient than before. |
