Summary of The Curse Of Diversity in Ensemble-based Exploration, by Zhixuan Lin et al.
The Curse of Diversity in Ensemble-Based Exploration
by Zhixuan Lin, Pierluca D’Oro, Evgenii Nikishin, Aaron Courville
First submitted to arxiv on: 7 May 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 The paper uncovers a surprising phenomenon in deep reinforcement learning, where training a diverse ensemble of data-sharing agents can significantly impair the performance of individual members compared to standard single-agent training. The authors attribute this degradation to the low proportion of self-generated data and inefficiency of individual agents to learn from highly off-policy data. They name this phenomenon “the curse of diversity.” To mitigate this issue, the authors explore intuitive solutions like larger replay buffers or smaller ensemble sizes, but find that these either fail to consistently improve performance or undermine ensembling’s advantages. Instead, they propose Cross-Ensemble Representation Learning (CERL) as a novel method to counteract the curse of diversity in both discrete and continuous control domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds that training many agents together can actually make individual agents worse at learning. This is because each agent only gets a small amount of its own data, while most of the time it’s just copying what others did. The authors show that this makes it hard for agents to learn from their own mistakes and improve over time. They tried some simple solutions like giving agents more storage or using fewer agents, but these didn’t work well either. Instead, they came up with a new way to train agents called Cross-Ensemble Representation Learning (CERL), which helps agents learn better even when they’re working together. |
Keywords
» Artificial intelligence » Reinforcement learning » Representation learning
