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Found 5 papers, 3 papers with code
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
In this paper, we propose RLExplorer, the first fault diagnosis approach for DRL-based software systems.
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Dr. DRL successfully helps agents to adapt to 19. 63% of drifted environments left unsolved by vanilla CL while maintaining and even enhancing by up to 45% the obtained rewards for drifted environments that are resolved by both approaches.
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
In this paper, we propose an empirical study on Stack Overflow (SO), the most popular Q&A forum for developers, to uncover and understand the challenges practitioners faced when deploying DRL systems.
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone.
An Empirical Study of Challenges in Converting Deep Learning Models
Usually DL models are developed and trained using DL frameworks that have their own internal mechanisms/formats to represent and train DL models, and usually those formats cannot be recognized by other frameworks.
