Summary of Reconciling Spatial and Temporal Abstractions For Goal Representation, by Mehdi Zadem et al.
Reconciling Spatial and Temporal Abstractions for Goal Representation
by Mehdi Zadem, Sergio Mover, Sao Mai Nguyen
First submitted to arxiv on: 18 Jan 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 Hierarchical Reinforcement Learning (HRL) algorithms can be improved by using goal representations that decompose complex learning problems into easier subtasks. Recent studies show that preserving temporally abstract environment dynamics is key to solving difficult problems, offering theoretical guarantees for optimality. However, these methods struggle when environment dynamics become increasingly complex. To address this, researchers have explored spatial abstraction, but this approach has limitations, including scalability issues in high-dimensional environments and reliance on prior knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hierarchical Reinforcement Learning algorithms can be made better by using goal representations that break down hard problems into smaller pieces. Scientists found that keeping track of how environment changes over time helps solve tough problems, making sure the solution is good enough. But this approach doesn’t work well when the problem gets too big and depends on knowing things beforehand. |
Keywords
* Artificial intelligence * Reinforcement learning
