Summary of Tldr: Unsupervised Goal-conditioned Rl Via Temporal Distance-aware Representations, by Junik Bae and Kwanyoung Park and Youngwoon Lee
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
by Junik Bae, Kwanyoung Park, Youngwoon Lee
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers propose a novel unsupervised goal-conditioned reinforcement learning (GCRL) method that leverages TemporaL Distance-aware Representations (TLDR). This approach aims to develop diverse robotic skills without external supervision. The method uses temporal distance to select faraway goals and compute intrinsic exploration rewards and goal-reaching rewards. The exploration policy seeks states with large temporal distances, while the goal-conditioned policy learns to minimize the temporal distance to the goal. The results demonstrate that TLDR significantly outperforms prior unsupervised GCRL methods in achieving a wide range of states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways for robots to learn and get better at doing things without being told what to do. The researchers came up with a new method called TemporaL Distance-aware Representations (TLDR) that helps robots explore and find their way to different goals. This approach uses something called temporal distance, which measures how far away the goal is. By using this idea, TLDR can help robots learn new skills and cover a wide range of states. |
Keywords
* Artificial intelligence * Reinforcement learning * Unsupervised
