reinforcement learning

Physics-informed reinforcement learning for energy-efficient minute-scale control of split-type air-conditioning systems: Experimental validation on real hardware

Deep reinforcement learning for adaptive rack repositioning and pod sequencing in robotic mobile fulfillment systems via multi-fidelity simulation optimization

Extension-layer fine-tuning for transfer learning in multi-agent reinforcement learning-based HVAC control

An SAC-Based Auto Optimization Model for Dynamic Current Balancing of Multichip Paralleled SiC Power Module

The dynamic current imbalance between the paralleled SiC mosfets in multichip power modules, which is commonly attributed to the asymmetric module layout, severely limits their current capacity and thermal reliability. Adjusting the connection points of bonding wires is an effective method to mitigate imbalanced dynamic current. However, manual trial-and-error is currently the most common method for optimizing connection points, which is both deficient and inefficient. Existing automated solutions usually rely on a prefitting process based on large datasets, which is time-consuming and impractical for high-dimensional parameter applications. Thus, this article proposes an optimization model to mitigate dynamic current imbalance, which can automatically adjust the connection points of bonding wires without any manual intervention. The reinforcement learning (RL) soft actor-critic algorithm was applied to the power module optimization, eliminating the need for prefitting and enabling high-dimensional parameter optimization. After optimization, nearly complete dynamic current balancing in both high-side and low-side switches in a multichip-paralleled half-bridge power module is achieved, as verified by simulations and experiments. This model achieves true dynamic current balancing automation for the first time, providing an important reference for the application of RL to the automated optimization of multichip power modules.

3D reconstruction of spatial transcriptomics data with dual incremental deep reinforcement learning

Weather-adaptive reinforcement learning framework for heat pump control under time-of-use tariffs

Deep reinforcement learning-based trajectory optimization for pressure vessel filament winding and analysis of multi-payout-eye co-layer hybrid winding

Anomaly detection method for satellite networks based on adaptive federated learning driven by deep reinforcement learning

Autonomous collaborative robotic manufacturing system with adaptive process optimization through multi-agent reinforcement learning for high precision components

Distributional reinforcement learning for digital twin-driven optimal sensor network configuration in structural monitoring

Tube-type flexible performance preset boundary-guided reinforcement learning control for input saturated nonlinear systems

Physics-informed simulation-based reinforcement learning for adaptive flow rate control in 3D concrete printing under equipment and rheological variability