Summary of Deep Attention Driven Reinforcement Learning (dad-rl) For Autonomous Decision-making in Dynamic Environment, by Jayabrata Chowdhury et al.
Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment
by Jayabrata Chowdhury, Venkataramanan Shivaraman, Sumit Dangi, Suresh Sundaram, P.B.Sujit
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Deep Attention Driven Reinforcement Learning (DADRL) framework addresses the challenge of autonomous vehicle decision making in urban environments by dynamically assigning and incorporating the significance of surrounding vehicles into the ego’s RL-driven decision-making process. The framework employs a context encoder to extract features from context maps, providing a comprehensive state representation that combines spatiotemporal representations with contextual encoding. Trained using the Soft Actor Critic (SAC) algorithm, DADRL outperforms recent state-of-the-art methods on the SMARTS urban benchmarking scenarios without traffic signals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous vehicles need to make smart decisions in busy cities. To do this, they must understand how different cars are moving around them. Current approaches use really powerful computers to figure this out, but it makes things slow and complicated. Our new approach is called DADRL, which uses attention mechanisms to focus on the most important interactions between vehicles. We also add a special layer that helps our model understand the context of the situation, like what’s happening on the roads around us. By combining these ideas, we can make better decisions faster and more efficiently. |
Keywords
» Artificial intelligence » Attention » Encoder » Reinforcement learning » Spatiotemporal
