multiobjective deep reinforcement learning多目标深度强化学习(Multiobjective Deep Reinforcement Learning,MDRL)是一种结合了深度学习和强化学习的方法,旨在解决具有多个优化目标的问题。在传统的强化学习中,通常只有一个目标,例如最大化累积奖励。然而,在许多实际应用中,可能需要同时考虑多个目标,例如在机器人控制中,可能...
深度学习在求解多目标优化问题(Multi-Objective Optimization, MOO)方面有着广泛的应用。多目标优化问题是指需要同时优化多个通常相互冲突的目标函数的问题。在深度学习的背景下,这些目标可能包括损失最小化、模型复杂度降低、稀疏性增加等。 多目标优化算法_IT猿手的博客-CSDN博客 深度强化学习(Deep Reinforcement Learning...
Thus, this paper proposes a multiobjective deep reinforcement learning method using decomposition and attention model to solve multiobjective optimization problem. In our method, each subproblem is solved by an attention model, which can exploit the structure features as well as node features of input...
reptile算法是一种经典的Model-Agnostic Meta-Learning(MAML)方法。 如何理解meta-learning:我们可以举个别的例子,比如我们要分辨猫,狗,鳄鱼等各种动物,我们会给每一个类别构造一个训练任务,然后meta-learning的目标是找到一种模型,对于给出的任何任务都能够胜任。 这里多目标学习的权重和子问题就是meta-learning中的子...
We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential ...
Abstract Introduction Related work Definition of the VM placement problem Multi-objective placement using deep reinforcement learning Numerical results Conclusions Change history References Acknowledgements Funding Author information Ethics declarations Additional information Rights and permissions About this articleDisc...
This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. The idea of decomposition is adopted to decompose a MOP into a set of scalar optimization subproblems. The subproblems are then optimized co...
Meta-Learning for Multi-objective Reinforcement LearningXi Chen 1 , Ali Ghadirzadeh 1,2 , Mårten Björkman 1 and Patric Jensfelt 1Abstract—Multi-objective reinforcement learning (MORL)is the generalization of standard reinforcement learning (RL)approaches to solve sequential decision making ...
The objective of this study is to improve Multi Objective Deep Reinforcement Learning (MODRL) for optimizing crowd guidance strategies. In general, MODRL i... R Nishida,Y Tanigaki,M Onishi,... - 《Proceedings of the Annual Conference of Jsai》 被引量: 0发表: 2022年 加载更多来源...
深度强化学习(Multi-Agent Deep Reinforcement Learning, MADRL)是强化学习(Reinforcement Learning, RL)和深度学习(Deep Learning, DL)的交叉领域,其中涉及多个智能体(agent)同时在环境中学习和交互。它尝试解决多智能体系统中的协调、竞争、通信等问题。与单智能体强化学习不同,多智能体系统中的智能体可能有不同的目...