Summary of Urban-focused Multi-task Offline Reinforcement Learning with Contrastive Data Sharing, by Xinbo Zhao et al.
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing
by Xinbo Zhao, Yingxue Zhang, Xin Zhang, Yu Yang, Yiqun Xie, Yanhua Li, Jun Luo
First submitted to arxiv on: 20 Jun 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 presents MODA, a novel approach for enhancing diverse human decision-making processes in an urban environment. By leveraging offline reinforcement learning (RL), MODA optimizes human urban strategies from pre-collected data. The method addresses two significant challenges: data scarcity and heterogeneity, as well as distributional shift. MODA achieves this through Contrastive Data Sharing among tasks, which extracts latent representations of human behaviors and shares data with similar representations. Additionally, the algorithm constructs a robust Markov Decision Process (MDP) using a dynamics model and Generative Adversarial Network (GAN). The results demonstrate significant improvements compared to state-of-the-art baselines, showcasing MODA’s potential in advancing urban decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MODA is a new way to help people make better decisions about transportation, like ride-sharing or public transit. It uses old data to learn what works well and applies that to new situations. The problem is that there isn’t always enough data, and the data can be different in different areas. MODA solves this by comparing similar data points and sharing information between related tasks. This helps make decisions more accurate and reliable. |
Keywords
» Artificial intelligence » Gan » Generative adversarial network » Reinforcement learning
