Summary of Discovering Multiple Solutions From a Single Task in Offline Reinforcement Learning, by Takayuki Osa and Tatsuya Harada
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
by Takayuki Osa, Tatsuya Harada
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 recent advancements in online reinforcement learning (RL) have shown the benefits of learning multiple behaviors from a single task for few-shot adaptation to new environments. However, this approach has not been thoroughly explored in offline RL, where finding multiple solutions from a single task is essential. The study proposes algorithms that can learn multiple solutions in offline RL and empirically evaluates their performance. The results demonstrate that the proposed algorithm learns multiple qualitatively and quantitatively distinctive solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning (RL) has made significant progress by adapting to new environments with few-shot learning. However, this approach hasn’t been applied to finding multiple solutions from a single task in offline RL. This study aims to address this gap by developing algorithms that can learn multiple solutions in offline RL and testing their performance. The outcome shows that the proposed algorithm successfully learns multiple distinctive solutions. |
Keywords
» Artificial intelligence » Few shot » Reinforcement learning
