Summary of Spatio-temporal Value Semantics-based Abstraction For Dense Deep Reinforcement Learning, by Jihui Nie and Dehui Du and Jiangnan Zhao
Spatio-temporal Value Semantics-based Abstraction for Dense Deep Reinforcement Learning
by Jihui Nie, Dehui Du, Jiangnan Zhao
First submitted to arxiv on: 24 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 Innovative abstract modeling is proposed for Intelligent Cyber-Physical Systems (ICPS) that incorporate Convolutional Neural Networks (CNNs) and Deep Reinforcement Learning (DRL). The approach uses spatial-temporal value semantics to capture the evolution of semantic value across time and space, constructing an abstract Markov Decision Process (MDP) for the DRL learning process. Optimization techniques refine the abstract model, mitigating semantic gaps between abstract and concrete states. The efficacy is evaluated through experiments involving lane-keeping, adaptive cruise control, and intersection crossroad assistance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ICPS use intelligent components like CNNs and DRL to do tasks like perception, decision-making, and control. They interact with the environment dynamically, trying to get cumulative rewards. But they face challenges like uncertainty, complexity, and data scarcity during learning. To solve this, researchers propose an abstract modeling approach that captures how semantic value changes over time and space. This helps build an abstract MDP for DRL, making it more efficient and generalizable. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning » Semantics
