reinforcement learning

Supplementary document for Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC - 8037132.pdf

8 pages and 7 sections

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Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC

We report an experimental implementation of deep reinforcement learning (RL) for optimizing the loading stage of a ⁶Li magneto-optical trap (MOT) in a high-dimensional continuous control space. An off-policy actor-critic agent observes in-situ fluorescence images at 111 ms intervals and updates seven experimental param...

Energy-efficient FPGA acceleration of reinforcement learning algorithms using high-level synthesis

The results show that pipelined fixed-point FPGA architectures are a good solution to speed up the reinforcement learning process in resource-limited embedded systems.

Reinforcement-Learning-Based Energy Management for a Range-Extended Distributed-Drive Tracked Combine Harvester in Hilly Terrain

Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed el...

HG-MARL: A Hierarchical Graph-Based Multi-Agent Reinforcement Learning Framework for Adaptive Policy Simulation in Circular Economies

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H3C-BEACON is presented, a unified hierarchical framework for coordination and control in cooperative MARL that jointly integrates communication, probabilistic belief inference, adaptive coalition formation, and policy stabilisation and significantly improves reproducibility across random initialisations.

Robust Multi-Objective Reinforcement Learning Improves Joint Predicted-Potency and Drug-Likeness Yield in EGFR Molecule Generation

Optimizing a molecular generator against a single activity-model estimate can reward structures for which that estimate is unreliable. We extended our published ChemBERTa–reinforcement-learning (RL) workflow for human epidermal growth factor receptor (EGFR) with scaffold-separated activity modeling, empirically calibra...

Supporting Continuous Improvement in Healthcare with Machine Learning

This project investigates the informatics architecture required to realize a continuous Learning Health System (LHS) in intensive care using offline reinforcement learning. Using mixed shock hemodynamic management as a focal use case, this work aligns organizational needs, algorithmic constraints, and end-user workflow...