The first purpose of this paper is to study the properties on some q-shift difference differential polynomials of meromorphic functions,some theorems about the zeros of some q-shift difference-differential polynomials...The first purpose of this paper is to study the properties on some q-shift difference differential polynomials of meromorphic functions,some theorems about the zeros of some q-shift difference-differential polynomials with more general forms are obtained.The second purpose of this paper is to investigate the properties on the Nevanlinna deficiencies for q-shift difference differential monomials of meromorphic functions,we obtain some relations amongδ(∞,f),δ(∞,f′),δ(∞,f(z)^(n)f(qz+c)^(m)f′(z)),δ(∞,f(qz+c);f′(z))andδ(∞,f(z)^(n)f(qz+c)^(m)).展开更多
离线强化学习旨在仅通过使用预先收集的离线数据集进行策略的有效学习,从而减少与环境直接交互所带来的高昂成本。然而,由于缺少环境对智能体行为的交互反馈,从离线数据集中学习到的策略可能会遇到数据分布偏移的问题,进而导致外推误差...离线强化学习旨在仅通过使用预先收集的离线数据集进行策略的有效学习,从而减少与环境直接交互所带来的高昂成本。然而,由于缺少环境对智能体行为的交互反馈,从离线数据集中学习到的策略可能会遇到数据分布偏移的问题,进而导致外推误差的不断加剧。当前方法多采用策略约束或模仿学习方法来缓解这一问题,但其学习到的策略通常较为保守。针对上述难题,提出一种基于自适应分位数的方法。具体而言,该方法在双Q估计的基础上进一步利用双Q的估计差值大小对分布外未知动作的价值高估情况进行评估,同时结合分位数思想自适应调整分位数来校正过估计偏差。此外,构建分位数优势函数作为策略约束项权重以平衡智能体对数据集的探索和模仿,从而缓解策略学习的保守性。最后在D4RL(datasets for deep data-driven reinforcement learning)数据集上验证算法的有效性,该算法在多个任务数据集上表现优异,同时展现出在不同场景应用下的广泛潜力。展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.11761035,12161074)the Natural Science Foundation of Jiangxi Province(Grant No.20181BAB201001)the Foundation of Education Department of Jiangxi Province(Grant Nos.GJJ190876,GJJ202303,GJJ201813,GJJ191042)。
文摘The first purpose of this paper is to study the properties on some q-shift difference differential polynomials of meromorphic functions,some theorems about the zeros of some q-shift difference-differential polynomials with more general forms are obtained.The second purpose of this paper is to investigate the properties on the Nevanlinna deficiencies for q-shift difference differential monomials of meromorphic functions,we obtain some relations amongδ(∞,f),δ(∞,f′),δ(∞,f(z)^(n)f(qz+c)^(m)f′(z)),δ(∞,f(qz+c);f′(z))andδ(∞,f(z)^(n)f(qz+c)^(m)).
文摘离线强化学习旨在仅通过使用预先收集的离线数据集进行策略的有效学习,从而减少与环境直接交互所带来的高昂成本。然而,由于缺少环境对智能体行为的交互反馈,从离线数据集中学习到的策略可能会遇到数据分布偏移的问题,进而导致外推误差的不断加剧。当前方法多采用策略约束或模仿学习方法来缓解这一问题,但其学习到的策略通常较为保守。针对上述难题,提出一种基于自适应分位数的方法。具体而言,该方法在双Q估计的基础上进一步利用双Q的估计差值大小对分布外未知动作的价值高估情况进行评估,同时结合分位数思想自适应调整分位数来校正过估计偏差。此外,构建分位数优势函数作为策略约束项权重以平衡智能体对数据集的探索和模仿,从而缓解策略学习的保守性。最后在D4RL(datasets for deep data-driven reinforcement learning)数据集上验证算法的有效性,该算法在多个任务数据集上表现优异,同时展现出在不同场景应用下的广泛潜力。
基金supported by the Natural Science Foundation of Jiang-Xi Province in China(2010GQS0119)(2009GQS0013)the Youth Foundation of Education Bureau of Jiangxi Province in China(GJJ11072)
