📝 Original Paper Info
- Title: Sat-EnQ Satisficing Ensembles of Weak Q-Learners for Reliable and Compute-Efficient Reinforcement Learning
- ArXiv ID: 2512.22910
- Date: 2025-12-28
- Authors: Ünver Çiftçi
📝 Abstract
Deep Q-learning algorithms remain notoriously unstable, especially during early training when the maximization operator amplifies estimation errors. Inspired by bounded rationality theory and developmental learning, we introduce Sat-EnQ, a two-phase framework that first learns to be ``good enough'' before optimizing aggressively. In Phase 1, we train an ensemble of lightweight Q-networks under a satisficing objective that limits early value growth using a dynamic baseline, producing diverse, low-variance estimates while avoiding catastrophic overestimation. In Phase 2, the ensemble is distilled into a larger network and fine-tuned with standard Double DQN. We prove theoretically that satisficing induces bounded updates and cannot increase target variance, with a corollary quantifying conditions for substantial reduction. Empirically, Sat-EnQ achieves 3.8x variance reduction, eliminates catastrophic failures (0% vs 50% for DQN), maintains 79% performance under environmental noise}, and requires 2.5x less compute than bootstrapped ensembles. Our results highlight a principled path toward robust reinforcement learning by embracing satisficing before optimization.
💡 Summary & Analysis
1. **Importance of Data Augmentation**: A technique that helps model learning when data is scarce, much like how a child understands better by solving the same problem in different situations.
2. **Superiority of Composite Methods**: Combining various augmentation techniques makes models stronger than using them individually, akin to consuming diverse foods for more effective nutrient intake.
3. **Variability in Results Across Datasets**: Different datasets might benefit from specific augmentation methods, similar to choosing appropriate clothing based on weather conditions.
📄 Full Paper Content (ArXiv Source)
1. **Importance of Data Augmentation**: A technique that helps model learning when data is scarce, much like how a child understands better by solving the same problem in different situations.
2. **Superiority of Composite Methods**: Combining various augmentation techniques makes models stronger than using them individually, akin to consuming diverse foods for more effective nutrient intake.
3. **Variability in Results Across Datasets**: Different datasets might benefit from specific augmentation methods, similar to choosing appropriate clothing based on weather conditions.
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