Summary of Policy Gradient For Robust Markov Decision Processes, by Qiuhao Wang et al.
Policy Gradient for Robust Markov Decision Processes
by Qiuhao Wang, Shaohang Xu, Chin Pang Ho, Marek Petrik
First submitted to arxiv on: 29 Oct 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 presents a novel policy gradient method called Double-Loop Robust Policy Mirror Descent (DRPMD) for solving robust Markov Decision Processes (MDPs). The method uses a generic policy gradient update rule with adaptive tolerance per iteration, ensuring convergence to a globally optimal policy. DRPMD is designed to handle model ambiguity and provides new insights into the inner problem solution through Transition Mirror Ascent (TMA). The paper also proposes innovative parametric transition kernels for both discrete and continuous state-action spaces, increasing the approach’s applicability. Empirical results validate the robustness and global convergence of DRPMD across various challenging robust MDP settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make computers learn good decisions when they’re not sure about what will happen next. This is called a “robust” decision-making method, which helps computers make better choices even if the world is uncertain or unpredictable. The new method, called DRPMD, uses a special kind of update rule that makes sure it finds the best possible solution. The paper also shows how this method can be used in different types of situations and with different kinds of information. |
