Summary of Learning Hidden Subgoals Under Temporal Ordering Constraints in Reinforcement Learning, by Duo Xu et al.
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning
by Duo Xu, Faramarz Fekri
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 This paper proposes a novel reinforcement learning (RL) algorithm called LSTOC for learning hidden subgoals under temporal ordering constraints. In real-world applications, completing a task often requires achieving multiple key steps in a specific time order, such as following a recipe. However, previous RL algorithms struggle to solve tasks with unknown or hidden subgoals and their temporal orderings. The authors introduce a new contrastive learning objective that learns hidden subgoals and their temporal orderings simultaneously using first-occupancy representation and temporal geometric sampling. Additionally, they propose a sample-efficient learning strategy that discovers subgoals one-by-one following their temporal ordering constraints by building a subgoal tree. This framework is evaluated on several image-based environments, showing significant improvements over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots learn to follow recipes or complete tasks in the right order. Imagine trying to make a cake without knowing the correct steps to take! The authors created a new way for machines to figure out what they need to do and when. They also made it more efficient by allowing machines to learn as they go, rather than having to try everything at once. |
Keywords
* Artificial intelligence * Reinforcement learning
