Summary of Selfbc: Self Behavior Cloning For Offline Reinforcement Learning, by Shirong Liu et al.
SelfBC: Self Behavior Cloning for Offline Reinforcement Learning
by Shirong Liu, Chenjia Bai, Zixian Guo, Hao Zhang, Gaurav Sharma, Yang Liu
First submitted to arxiv on: 4 Aug 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 investigates the limitations of traditional policy constraint methods in offline reinforcement learning, which tend to result in overly conservative policies. The authors attribute this limitation to the static nature of traditional constraints and propose a novel dynamic policy constraint that restricts the learned policy based on samples generated by the exponential moving average of previously learned policies. This approach facilitates the learning of non-conservative policies while avoiding policy collapse in the offline setting, achieving state-of-the-art performance among policy constraint methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is trying to figure out how to make better decisions using data collected beforehand. But sometimes, this makes decisions too safe and not good enough. This paper tries to fix that by making a new rule for decision-making that takes into account what other similar decisions were made in the past. This helps make better decisions without getting stuck in a routine. The researchers tested their idea on some complex tasks and it worked really well! |
Keywords
» Artificial intelligence » Reinforcement learning
