For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with c...For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.展开更多
目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根...目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根据每次入院时BMI和代谢情况分为BMI正常、超重和代谢综合征3种状态,采用多状态Markov模型分析转移规律。结果纳入398例研究对象的892条观测记录,中位年龄56岁,中位随访时间40个月。结果显示3种状态间共发生494次转移,其中5.1%由BMI正常转移为超重,5.5%由超重转移为MetS。超重发展为MetS的转移强度最高,是超重变为BMI正常的9.52倍。48.53个月后,BMI正常的抑郁症患者开始转移为MetS。对于超重的患者,8.77个月后开始转移为MetS。36个月后,BMI正常或超重者转移为MetS的概率为31.4%和50.4%;对于合并MetS者,36个月后仍为MetS的概率为51.2%。多因素分析显示未婚是体质量正常的抑郁症患者转移为超重的危险因素,而具有较高的受教育程度是超重的抑郁症患者转移为MetS的保护因素。结论抑郁症患者发展为MetS的强度和风险较高,发生MetS后不易好转,提示加强抑郁症患者的BMI管理和MetS的干预。展开更多
基金supported by the National Natural Science Foundation of China (61070143 61173088)
文摘For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.
文摘目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根据每次入院时BMI和代谢情况分为BMI正常、超重和代谢综合征3种状态,采用多状态Markov模型分析转移规律。结果纳入398例研究对象的892条观测记录,中位年龄56岁,中位随访时间40个月。结果显示3种状态间共发生494次转移,其中5.1%由BMI正常转移为超重,5.5%由超重转移为MetS。超重发展为MetS的转移强度最高,是超重变为BMI正常的9.52倍。48.53个月后,BMI正常的抑郁症患者开始转移为MetS。对于超重的患者,8.77个月后开始转移为MetS。36个月后,BMI正常或超重者转移为MetS的概率为31.4%和50.4%;对于合并MetS者,36个月后仍为MetS的概率为51.2%。多因素分析显示未婚是体质量正常的抑郁症患者转移为超重的危险因素,而具有较高的受教育程度是超重的抑郁症患者转移为MetS的保护因素。结论抑郁症患者发展为MetS的强度和风险较高,发生MetS后不易好转,提示加强抑郁症患者的BMI管理和MetS的干预。
