Summary of Multi-agent Reinforcement Learning For Autonomous Driving: a Survey, by Ruiqi Zhang et al.
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
by Ruiqi Zhang, Jing Hou, Florian Walter, Shangding Gu, Jiayi Guan, Florian Röhrbein, Yali Du, Panpan Cai, Guang Chen, Alois Knoll
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Robotics (cs.RO)
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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 The proposed research tackles the complexities of multi-agent reinforcement learning (MARL) in real-world applications, such as autonomous driving. The authors highlight the challenges of designing algorithms that consider interactions between multiple agents, mutual influences, and resource distribution. To overcome these hurdles, they introduce a set of metrics for simulators and summarize existing benchmarks. Additionally, the study reviews foundational knowledge and synthesizes recent advancements in MARL-related areas like intelligent transportation systems. Specifically, it examines environmental modeling, state representation, perception units, and algorithm design in autonomous driving applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on using Reinforcement Learning (RL) for making decisions in complex situations. It’s a big deal because RL can already do things better than humans in many areas! The problem is that when there are multiple agents working together, like self-driving cars, it gets much harder to design good algorithms. This paper tries to solve this problem by coming up with new ways to measure how well simulators work and summarizing what’s been done so far in the field. It also talks about some cool applications of these ideas in autonomous driving. |
Keywords
* Artificial intelligence * Reinforcement learning
