state-of-the-art approaches to optimization-based meta-learning: classical long short-term memory (LSTM) meta-learner; model-agnostic meta-learning (MAML) and its variations in few-shot learning, reinforcement learning, and intimation learning; first-order model-agnostic meta-learning; and Reptile....
论文笔记:Meta-Learning-Based Deep Reinforcement Learningfor Multiobjective Optimization Problems 1.研究内容: 多目标组合优化问题: 在现实生活中,优化问题往往有不止一个维度,这些问题可以被建模为多目标优化问题,目标是获得一个种群大小的解集。 minx∈XF(x)=(f1(x),f2(x),...,fm(x))T 文章实际具体做...
In this paper, we combine context-based Meta-Reinforcement Learning with task-aware representation to efficiently overcome data-inefficiency and limited generalization in the hyperparameter optimization problem. First, we propose a new context-based meta-RL model that disentangles task inference and contr...
Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and st...
Model-Based Meta-Policy-Optimization MB-MPO通过meta-learning的方法最大限度提高策略的适应性,元学习策略可以通过一个策略梯度更新步骤快速适应任何动态模型(从一组模型中)。 Model Learning 同样通过model emsemble的方式学习world model(但是具体有些许不同)。每个模型的初始化参数不同,真实环境样本分别存在不同的buf...
So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain ...
Meta-heuristicsTeaching–learning-based optimizationIn this paper, teaching鈥搇earning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop ...
Hence, to address these issues, an innovative solution is proposed in this study by leveraging the benefits of the African Buffalo Optimization algorithm and Convolutional Neural Network. The novelty of the approach lies in the seamless integration of deep learning and meta-heuristic optimization algori...
此外,利用元学习(meta-learning)和自监督学习(self-supervised learning)开发更具泛化性的优化方法,有助于提升智能体在不同任务中的表现。优化奖励设计并集成以效率为中心的算法,将在降低计算成本的同时保持高适应性方面发挥关键作用。 7.3 跨领域适应能力 跨领域泛化能力对于 LLM 智能体在真实应用中的成功至关重要,...
In the era of big data, data-driven machine-learning approaches have emerged as viable alternatives to first-principles models within model-based optimization formulations. This advancement facilitates the practical application of model-based optimization across various industries, significantly enhancing its...