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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Minimal Ingredients for Reward Assignment from Expert Demonstrations
TL;DR
This research identifies the essential components for effective reward assignment in imitation learning, revealing that proximity alone suffices in offline settings, while temporal alignment is crucial in online scenarios. These insights promote a minimalist approach to reward design.
✦ Why It Matters
Focus on proximity in reward design to enhance offline imitation learning performance immediately.
Key Takeaways
- Utilize proximity alone for effective offline reinforcement learning.
- Incorporate temporal alignment for online learning scenarios.
- Test reward structures across various benchmarks for robustness.
- Adopt a minimalist approach before complicating reward design.
How It Works
The study evaluates reward assignment by analyzing how closely learner trajectories match expert demonstrations. It establishes that in offline learning, simply measuring proximity is sufficient for effective reward assignment.
In contrast, online learning benefits from considering the timing of actions, which enhances performance when multiple demonstrations are present.