Summary of Exploring the Edges Of Latent State Clusters For Goal-conditioned Reinforcement Learning, by Yuanlin Duan et al.
Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning
by Yuanlin Duan, Guofeng Cui, He Zhu
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: Exploring unknown environments efficiently is crucial in unsupervised goal-conditioned reinforcement learning. The paper proposes “Cluster Edge Exploration” (CE^2), an algorithm that prioritizes choosing goals in sparsely explored areas of the state space, focusing on states that are easily reachable from one another by the current policy under training in a latent space. This approach ensures efficient exploration compared to baseline methods and ablations in challenging robotics environments such as navigating a maze with a multi-legged ant robot, manipulating objects with a robot arm on a cluttered tabletop, and rotating objects in the palm of an anthropomorphic robotic hand. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about finding new ways for robots to explore their surroundings. Sometimes, robots have trouble reaching new areas because they don’t know how to get there. The researchers came up with a new way called “Cluster Edge Exploration” that helps the robot choose the best path to reach these new areas. They tested it in different scenarios like navigating a maze or picking up objects, and it did better than other methods. |
Keywords
» Artificial intelligence » Latent space » Palm » Reinforcement learning » Unsupervised
