Summary of Offline Hierarchical Reinforcement Learning Via Inverse Optimization, by Carolin Schmidt et al.
Offline Hierarchical Reinforcement Learning via Inverse Optimization
by Carolin Schmidt, Daniele Gammelli, James Harrison, Marco Pavone, Filipe Rodrigues
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 OHIO, a framework for offline reinforcement learning (RL) of hierarchical policies. The authors address the challenge of learning hierarchical policies from static offline datasets by leveraging knowledge of the policy structure to recover unobservable high-level actions that likely generated the observed data under their hierarchical policy. This approach constructs a dataset suitable for off-the-shelf offline training, allowing for improved performance and robustness in sequential decision-making problems such as robotic and network optimization tasks. The authors demonstrate OHIO’s effectiveness through various instantiations of the framework, both in direct deployment of policies trained offline and when online fine-tuning is performed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from past experiences to make good decisions. It’s like trying to figure out what someone did in the past by looking at the results they got. The authors came up with a new way to do this, called OHIO, which can help us make better decisions even when we don’t have direct access to all the information. They tested it on different problems and showed that it works well, especially when we need to make good decisions over a long period of time. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Reinforcement learning
