Summary of Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning, by Aditya A. Ramesh et al.
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
by Aditya A. Ramesh, Kenny Young, Louis Kirsch, Jürgen Schmidhuber
First submitted to arxiv on: 6 May 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 proposed paper addresses the challenge of temporal credit assignment in reinforcement learning, where delayed and stochastic outcomes make it difficult to evaluate actions. The authors introduce a new approach called Chunked-TD, which uses predicted probabilities from a model to compute targets for temporal difference (TD) learning. This method is motivated by the principle of history compression, which “chunks” trajectories to speed up credit assignment while still bootstrapping when necessary. The proposed algorithms can be implemented online and show improved performance compared to conventional TD(lambda). The paper’s contributions include a new model-based solution that is less vulnerable to inaccuracies in world models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the right way to learn from our mistakes in a game or problem where we don’t get immediate feedback. It’s like trying to figure out what you did wrong when you didn’t win a level on your favorite video game until hours later. The authors have come up with a new method called Chunked-TD that helps us do this more efficiently by using predictions about the future to make better decisions. This can help us learn faster and make fewer mistakes. |
Keywords
» Artificial intelligence » Bootstrapping » Reinforcement learning
