Summary of Autonomous Option Invention For Continual Hierarchical Reinforcement Learning and Planning, by Rashmeet Kaur Nayyar and Siddharth Srivastava
Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planning
by Rashmeet Kaur Nayyar, Siddharth Srivastava
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed approach for inventing, representing, and utilizing options in reinforcement learning enables the scaling up of abstract state and action representations. This novel method tackles challenging continual RL settings characterized by long horizons, sparse rewards, and unknown transition and reward functions. By leveraging options as temporally extended behaviors, the approach promotes transfer and generalization across streams of stochastic problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is like teaching a robot to learn new tricks! But how can we teach it to do things that are really hard or take a long time? That’s where “options” come in. Options are like special behaviors that robots can learn to do, and they help the robot generalize and transfer its knowledge to new situations. This paper introduces a new way to use options to make reinforcement learning more efficient and scalable. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
