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Found 3 papers, 1 papers with code
Robotic Packaging Optimization with Reinforcement Learning
Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
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