Summary of Robust Model-based Reinforcement Learning with An Adversarial Auxiliary Model, by Siemen Herremans et al.
Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
by Siemen Herremans, Ali Anwar, Siegfried Mercelis
First submitted to arxiv on: 14 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 In this paper, researchers aim to improve the performance of reinforcement learning (RL) agents by introducing a novel learned transition model that incorporates an auxiliary pessimistic model. The proposed method, Robust Model-Based Policy Optimization (RMBPO), enhances policy robustness in high-dimensional MuJoCo control tasks by estimating the worst-case Markov decision process (MDP) within a Kullback-Leibler uncertainty set. By learning a pessimistic world model and demonstrating its role in improving policy robustness, this research contributes to making RL more robust. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is used to train robots, play board games, and even win at classic video games. But these agents often struggle when faced with slightly different situations. To fix this, researchers created a new way of thinking about Markov decision processes (MDPs). They called it Robust MDPs (RMDPs) and used a special kind of model to help the agent prepare for unexpected challenges. By testing their idea on robots and other tasks, they showed that it makes the agents much better at handling unknown situations. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
