Summary of A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges, by Guiliang Liu et al.
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
by Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 paper presents a comprehensive survey of advances in Inverse Constrained Reinforcement Learning (ICRL), which aims to infer the implicit constraints that expert agents adhere to based on their demonstration data. The authors formally define the problem and outline an algorithmic framework for constraint inference across various scenarios, including deterministic or stochastic environments, limited demonstrations, and multiple agents. They illustrate critical challenges and introduce fundamental methods to tackle these issues in discrete, virtual, and realistic environments. Applications of ICRL include autonomous driving, robot control, and sports analytics. The survey concludes with a discussion of key unresolved questions that can foster a bridge between theoretical understanding and practical industrial applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn how experts make decisions, by looking at what they do and trying to figure out the rules they follow. It’s like trying to solve a puzzle! The authors are very good at explaining this idea and show how it can be used in different situations, like making self-driving cars or robots. They also talk about why this is important and what we still need to learn. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
