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Approximate strategies for learning trajectories of autonomous learning agents
A. Mete Cakmakci,
Can Isik
Department of Electrical Engineering & Computer Science
Research output
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Chapter in Book/Entry/Poem
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Conference contribution
1
Scopus citations
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Keyphrases
Learning Trajectories
100%
Autonomous Learning Agents
100%
Performance States
50%
Coordination Problems
50%
Supervised Learning
50%
Current Performance
50%
Learning Dynamics
50%
Learning Agents
50%
Performance Improvement
50%
Performance Goals
50%
Learning Structure
50%
Target Trajectory
50%
Modular Learning
50%
Computer Science
Learning Agent
100%
Learning Trajectory
100%
Performance Improvement
33%
Performance State
33%
Supervised Learning
33%
structure learning
33%
Learning Process
33%
Performance Goal
33%
Chemical Engineering
Supervised Learning
100%