新闻
When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers introduced OVI, an imitation learning algorithm that uses value function estimation and expert interaction to reduce representational demands on learner models.
- 为何重要
- Matters for robotics engineers and ML practitioners building systems that learn from expert demonstrations with limited model capacity.
- 注意
- OVI requires access to a linear maximization oracle and assumes the learner can represent the expert's value function, which may not hold in all practical settings.
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