Summary of Last-iterate Global Convergence Of Policy Gradients For Constrained Reinforcement Learning, by Alessandro Montenegro and Marco Mussi and Matteo Papini and Alberto Maria Metelli
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
by Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli
First submitted to arxiv on: 15 Jul 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 general framework for addressing Constrained Reinforcement Learning (CRL) problems via gradient-based primal-dual algorithms. CRL involves sequential decision-making where agents must achieve goals while meeting domain-specific constraints formulated as expected costs. Policy-based methods are widely used in CRL due to their advantages in continuous-control problems. The authors introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-iterate convergence guarantees under (weak) gradient domination assumptions. This improves and generalizes existing results. The paper also presents two variants of C-PG: C-PGAE for action-based exploration and C-PGPE for parameter-based exploration. These algorithms naturally extend to constraints defined in terms of risk measures over the costs, as requested in safety-critical scenarios. Numerical experiments validate the effectiveness of these algorithms on constrained control problems, outperforming state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that makes decisions based on what it has learned from experience. This paper is about making sure this program follows certain rules, or “constraints”, while still trying to achieve its goals. The authors came up with a new way to do this, using something called primal-dual algorithms. Their method, called C-PG, can ensure that the program always makes good choices and follows the rules. They also created two versions of their algorithm that work differently, depending on how the program learns from experience. The results show that these algorithms are really effective at solving problems where constraints need to be followed. |
Keywords
» Artificial intelligence » Reinforcement learning
