Summary of Learning the Target Network in Function Space, by Kavosh Asadi et al.
Learning the Target Network in Function Space
by Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor
First submitted to arxiv on: 3 Jun 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 proposes a novel algorithm, Lookahead-Replicate (LR), for approximating value functions in reinforcement learning (RL). Unlike existing methods that rely on parameter-space equivalence between online and target networks, LR maintains function-space equivalence through a new target-network update. The authors demonstrate the effectiveness of LR by showing its convergence in learning value functions and empirical results indicating significant improvements in deep RL on the Atari benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to learn from experiences in games or simulations (reinforcement learning). Right now, this task is often solved by updating two networks that are similar. The new idea is to update these networks not based on what they look like, but rather based on what they do. This helps the algorithm learn faster and better. The paper shows that this new approach works well and can be used to improve game-playing AI. |
Keywords
» Artificial intelligence » Reinforcement learning
