An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems

Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirable behaviors due to environmental drifts, which often occur in constantly-changing production settings. Continual Learnin...

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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 1314 - 1325
Hauptverfasser: Yahmed, Ahmed Haj, Bouchoucha, Rached, Braiek, Houssem Ben, Khomh, Foutse
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 11.09.2023
Schlagworte:
Behavioral sciences
Continual Learning
Deep learning
Deep Reinforcement Learning
Intentional Forgetting
Neurons
Production
Reinforcement learning
Social networking (online)
Software Healing
Training
ISSN:2643-1572
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Zusammenfassung:Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirable behaviors due to environmental drifts, which often occur in constantly-changing production settings. Continual Learning (CL) is the inherent self-healing approach for adapting the DRL agent in response to the environment's conditions shifts. However, successive shifts of considerable magnitude may cause the production environment to drift from its original state. Recent studies have shown that these environmental drifts tend to drive CL into long, or even unsuccessful, healing cycles, which arise from inefficiencies such as catastrophic forgetting, warm-starting failure, and slow convergence. In this paper, we propose Dr. DRL, an effective self-healing approach for DRL systems that integrates a novel mechanism of intentional forgetting into vanilla CL (i.e., standard CL) to overcome its main issues. Dr. DRL deliberately erases the DRL system's minor behaviors to systematically prioritize the adaptation of the key problem-solving skills. Using well-established DRL algorithms, Dr. DRL is compared with vanilla CL on various drifted environments. Dr. DRL is able to reduce, on average, the healing time and fine-tuning episodes by, respectively, 18.74% and 17.72%. 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.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00121