Two timescale stochastic approximation (SA) has been widely used in value-based reinforcement learning algorithms. In the policy evaluation setting, it can model the linear and nonlinear temporal difference lea
Below is the taxonomy of reinforcement learning algorithms. Solid line indicates some progression from one idea to another. Dashed line indicates a loose connection. On the bottom you can see the timeline of the publication year of the algorithms....
Learning algorithms Learning Theory Machine Learning Stochastic Learning and Adaptive Control 1 Introduction We have recently seen astounding achievements by reinforcement learning (RL) agents. In games like Go, Chess, Shogi, and Atari, RL agents have outperformed human players (Silver et al., 20...
Application of two promising reinforcement learning algorithms for load shifting in a cooling supply system Energy Build., 229 (2020), p. 110490, 10.1016/j.enbuild.2020.110490 View PDFView articleView in ScopusGoogle Scholar [65] B. Liu, M. Akcakaya, T.E. Mcdermott Automated control of trans...
We also review the most outstanding papers by category and the strategies and algorithms proposed for the most relevant activities: batching, routing, sequencing, waiting, and assigning. To conclude, we outline the open issues and future paths of the topic under study....
IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is
At present, FL combined with Multi-task Learning (Smith et al.2017), Reinforcement Learning (Qi et al.2021), Graph Neural Network (Wu et al.2021) or other artificial intelligence algorithms have been proposed and applied in many fields. In addition, similar to FL, some collaborative learning...
35d2293 Breadcrumbs cs249r_book / taxonomy.qmd Latest commit HistoryHistoryFile metadata and controls Code Blame 9 lines (5 loc) · 114 Bytes Raw 1 2 3 4 5 6 7 8 9 # Taxonomy of ML Algorithms ## Supervised Learning ## Unsupervised Learning ## Reinforcement Learning ## Quiz...
With the development of deep learning algorithms and computer hardware, advances have been made in sence image translation [209–212], remote sensing image caption [213–215], etc. While cross-modal translation is an emerging task in remote sensing, an extensive range of algorithms have been ...
Learning algorithms Optimization 1Introduction Modern applications in industry, business, and information systems require a tremendous amount of optimization. Global optimization (GO) tackles various severe problems emerging from the context of complex physical systems, business processes, and particular from ...