Publications by authors named "Shuozhe Wang"
Article Synopsis
- Reinforcement learning based hyper-heuristics (RL-HH) merge the strengths of hyper-heuristics (for broad optimization) with reinforcement learning (for adaptive learning), enhancing the ability to solve complex problems.
- This research provides a comprehensive overview of RL-HH, including a categorized framework that distinguishes between value-based and policy-based algorithms, detailing common examples in each group.
- The article also identifies gaps in current RL-HH research and suggests potential directions for future studies to improve the field.
Construction of Mn doped CuS nanozymes for synergistic tumor therapy in NIR-I/II bio-windows.
Colloids Surf B Biointerfaces
February 2024
In photothermal therapy (PTT) and chemodynamic therapy (CDT) of cancer, poor performance of nanoagents severely impaired the therapeutic effect of cancer. To solve the problem, we proposed and constructed a novel Mn doped CuS phothermal nanoagent both in the first near-infrared (NIR-I) and the second near- infrared (NIR-II) windows in this work, which exhibited high photothermal conversion efficiency of 40.3% at 808 nm (NIR-I window) and 33.
View Article and Find Full Text PDFIn this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent toolkit. By multiphase learning, we split the traditional single training phase into multiple consecutive training phases, where the performance level of the strong team for each phase increases in an incremental way.
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