Summary of How to Explore with Belief: State Entropy Maximization in Pomdps, by Riccardo Zamboni et al.
How to Explore with Belief: State Entropy Maximization in POMDPs
by Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti
First submitted to arxiv on: 4 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 proposed research generalizes state entropy maximization in reinforcement learning from fully observable to partially observable environments, where agents receive incomplete information about the system’s state. The authors develop a policy gradient method to optimize the entropy of true states given only partial observations, which is a relaxation of the problem defined on belief states. This work aims to address challenges in more realistic domains and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to solve a big problem in machine learning. Right now, computers can’t learn very well when they don’t have all the information about what’s going on. Imagine you’re controlling a robot that can only see part of the room – it would be hard for the robot to figure out where everything is! The researchers are coming up with new ways to solve this problem by using a type of computer learning called policy gradient. They want to make sure computers can learn in situations where they don’t have all the information. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
