Summary of Raddqn: a Deep Q Learning-based Architecture For Finding Time-efficient Minimum Radiation Exposure Pathway, by Biswajit Sadhu et al.
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure Pathway
by Biswajit Sadhu, Trijit Sadhu, S. Anand
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 a deep Q-learning based architecture called RadDQN that addresses the challenges of implementing deep reinforcement learning (DRL) in the nuclear industry for optimizing radiation exposure. The authors propose a radiation-aware reward function and unique exploration strategies to provide time-efficient minimum radiation-exposure pathways in a radiation zone. They benchmark their approach against a grid-based deterministic method, demonstrating superior convergence rates and training stability compared to vanilla DQN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists use artificial intelligence (AI) to help keep people safe from radiation while working with nuclear materials. The researchers create a new way for computers to learn how to make decisions about the best route to take in areas where there is a lot of radiation. This helps the computers find the safest path quickly and efficiently. |
Keywords
» Artificial intelligence » Reinforcement learning
