Summary of Temporal Abstraction in Reinforcement Learning with Offline Data, by Ranga Shaarad Ayyagari et al.
Temporal Abstraction in Reinforcement Learning with Offline Data
by Ranga Shaarad Ayyagari, Anurita Ghosh, Ambedkar Dukkipati
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper addresses the challenge of training hierarchical reinforcement learning (RL) models that can learn complex behaviors in sparse-reward environments. Standard RL algorithms struggle with tasks requiring long-term planning, diverse behaviors, or temporal abstraction. To overcome this limitation, the options framework was introduced to train a hierarchy of policies over different time scales. However, training these algorithms requires high sample complexity, making them impractical for online settings. The proposed offline hierarchical RL method tackles this issue by learning options from existing offline datasets collected by unknown agents. This is achieved through a framework that trains an online hierarchical RL algorithm on an offline dataset of transitions. The method is evaluated on various environments, including locomotion tasks and robotic gripper block-stacking tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve artificial intelligence (AI) systems that can learn complex behaviors in situations where rewards are hard to come by or require planning ahead. Current AI models struggle with these challenges, which is why researchers are working on new methods. One approach involves breaking down complex tasks into smaller, more manageable steps. The challenge lies in finding a way to train these step-by-step plans using existing data collected by other unknown agents. This paper proposes a solution that can learn from this existing data and apply it to new situations. |
Keywords
» Artificial intelligence » Reinforcement learning
