Summary of Offline Reinforcement Learning From Datasets with Structured Non-stationarity, by Johannes Ackermann et al.
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
by Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to Offline Reinforcement Learning (RL) that addresses the challenge of non-stationarity in the offline dataset. The authors develop a Contrastive Predictive Coding-based method that identifies and accounts for gradual changes in transition and reward functions between episodes, while staying constant within each episode. This allows the policy to be trained on an offline dataset and still achieve high performance during evaluation. The proposed method is tested on simple continuous control tasks and challenging locomotion tasks, showing promising results that often match or exceed oracle performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in artificial intelligence called Reinforcement Learning. Right now, AI needs lots of data to learn and make good decisions. But what if we could teach the AI using data collected by someone else? That’s what Offline RL is all about! The authors come up with a new way to deal with a tricky issue where the rules change between episodes but stay the same within each episode. They use a special technique that helps the AI understand this change and make better decisions. This could be really helpful in areas like robotics or video games. |
Keywords
» Artificial intelligence » Reinforcement learning
