Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control

Researchers introduce SODACER, a reinforcement learning framework combining dual-buffer experience replay with Control Barrier Functions to enable safe optimal control of nonlinear systems. The approach demonstrates improved convergence and sample efficiency while maintaining safety constraints, with potential applications in robotics, healthcare, and large-scale optimization.

SODACER represents a meaningful advancement in safe reinforcement learning by addressing a fundamental tension in RL systems: the need to learn efficiently from past experiences while avoiding catastrophic failures in safety-critical domains. The dual-buffer architecture elegantly separates concerns—the Fast-Buffer captures recent behavioral shifts while the Slow-Buffer maintains diverse historical context through adaptive clustering. This separation directly tackles the exploration-exploitation tradeoff that has limited RL deployment in real-world applications.

The integration with Control Barrier Functions addresses a critical gap in current RL research. Most reinforcement learning frameworks optimize for task performance without formal safety guarantees, making them unsuitable for applications like autonomous systems or medical devices where constraint violations carry severe consequences. By embedding CBFs into the learning process, SODACER provides mathematical guarantees that safety constraints remain satisfied throughout training, not just at convergence.

For the AI development community, this work signals growing maturity in bridging theoretical safety guarantees with practical learning algorithms. The validation against an HPV transmission model demonstrates applicability beyond traditional control domains into epidemiological systems, suggesting broader relevance for modeling complex biological processes. The statistical rigor of using Friedman tests for comparative evaluation strengthens credibility.

The framework's generalizability matters significantly for organizations building autonomous systems. By providing a reusable architecture that combines memory efficiency, safety guarantees, and convergence speed, SODACER could accelerate deployment of RL systems in healthcare robotics and industrial optimization where regulatory and ethical constraints demand formal safety verification. Future developments will likely focus on scaling SODACER to higher-dimensional state spaces and more complex constraint sets.

• →SODACER combines dual-buffer experience replay with adaptive clustering to improve sample efficiency and convergence in reinforcement learning.
• →Integration with Control Barrier Functions provides formal mathematical safety guarantees for constrained optimization tasks.
• →The framework demonstrates superior performance against baseline methods while maintaining safe trajectories through Friedman statistical testing.
• →Architecture enables practical deployment in safety-critical domains including robotics, healthcare, and epidemic control systems.
• →Self-organizing adaptive clustering dynamically removes redundant experiences, optimizing memory usage without sacrificing learning performance.