Allison Moore
Papers
1
Total Citations
8
H-Index
1
About
Allison Moore is a leading researcher in Interactive Reinforcement Learning (IntRL), with a focus on bridging the gap between human teaching and autonomous robotic learning. Her most-cited work, "Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments" (2022, 8 citations), tackles a critical limitation in IntRL: its historical restriction to discrete-action spaces. By developing methods that allow human teachers to provide real-time feedback in continuous action environments, Moore has expanded the applicability of IntRL to more complex, real-world robotic tasks. Her contributions are foundational for creating robots that learn faster and more intuitively from human guidance, directly addressing the challenge of integrating human oversight into autonomous systems. While her citation count is still growing, her work is already recognized for its practical implications in human-robot interaction and adaptive learning systems. Moore's research is paving the way for safer, more collaborative AI, making her a rising voice in the field of interactive machine learning.
Research Focus
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Top Papers
- 1