Summary of State-free Reinforcement Learning, by Mingyu Chen et al.
State-free Reinforcement Learning
by Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang
First submitted to arxiv on: 27 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 A novel algorithm for state-free reinforcement learning (RL) is proposed, which learns to interact with an environment without prior knowledge of its states. The designed algorithm achieves a regret that is independent of the state space and only depends on the reachable state set. This research takes a concrete step towards parameter-free RL, aiming to develop algorithms that require no hyper-parameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new type of reinforcement learning algorithm that doesn’t need information about the environment’s states beforehand. The algorithm works by focusing on the states it can reach and achieves good results without needing to know all the possible states. This is an important step towards creating algorithms that don’t require as much tuning. |
Keywords
* Artificial intelligence * Reinforcement learning
