Policy learning is the increased understanding that occurs when policymakers compare one set of policy problems to others within their own or in other jurisdictions. It can aid in understanding why a policy was implemented, the policy's effects, and how the policy could apply to the policymakers' jurisdiction.[1] Before a policy is adopted it goes through a process that involves various combinations of elected official(s), political parties, civil servants, advocacy groups, policy experts or consultants, corporations, think tanks, and multiple levels of government. Policy can be challenged in various ways, including questioning its legality. Ideally, policymakers develop complete knowledge about the policy; the policy should achieve its intent and efficiently use resources.[2]
Policy learning through globalization has helped government organizations become more competitive.[3] Policymakers have easy access to global policy knowledge through the internet, access to think tanks, international institutions such as the United Nations, International Monetary Fund (IMF) or the World Bank and individual experts.[3][4]
^Moran, Michael; Rein, Martin; Goodin, Robert (2009). The Oxford handbook of public policy. USA, New York: Oxford University Press Inc. ISBN 978-0-19-926928-0.
^Dolowitz, David; Marsh, David (2000). "Learning from Abroad: The Role of Policy Transfer in Contemporary Policy - Making". Governance. 13: 5–24. doi:10.1111/0952-1895.00121 – via Scholars Portal Journals.
^ abSeabrooke, Leonard (2012). "Pragmatic numbers: the IMF, financial reform and policy learning in least likely environments". Journal of International Relations and Development. 15 (4): 486–505. doi:10.1057/jird.2012.2. S2CID 256513823. ProQuest 1128094526.
^Common, Richard (2004). "Organisational learning in a political environment". Policy Studies. 25: 35–49. doi:10.1080/0144287042000208224. S2CID 154092441 – via Scholars Portal Journals.
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...
Policylearning is the increased understanding that occurs when policymakers compare one set of policy problems to others within their own or in other...
classical reinforcement learning, the goal of such an agent is to learn a function that guides its behavior called a policy. This function learns to...
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...
Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...
the student is learning one foreign language and choosing the different stream after 8th class. The NEP 2020 replaces the National Policy on Education of...
controversial, the policy called for the use and learning of Hindi to be encouraged uniformly to promote a common language for all Indians. The policy also encouraged...
1177/0002716204272652. S2CID 154759501. Meseguer, Covadonga (2005). "PolicyLearning, Policy Diffusion, and the Making of a New Order". The Annals of the American...
Blended learning or hybrid learning, also known as technology-mediated instruction, web-enhanced instruction, or mixed-mode instruction, is an approach...
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
non-formal learning contexts with a lifelong learning approach, and to ensure that data are collected on the most excluded. Education policy in Brazil...
the transition probabilities are needed in value and policy iteration. In reinforcement learning, instead of explicit specification of the transition...
Milan; den,236 Hartog, Jerry (October 2012). "A machine learning solution to assess privacy policy completeness". Proceedings of the 2012 ACM workshop on...
state of the MDP. A positive learning rate α {\displaystyle \alpha } is chosen. We then repeatedly evaluate the policy π {\displaystyle \pi } , obtain...
developed policies for life-long learning which focus strongly on the need to identify, assess and certify non-formal and informal learning, particularly...
focus to include the design of curricula, informal learning environments, instructional methods, and policy innovations. As an emerging discipline, LS is still...
Lifelong learning is the "ongoing, voluntary, and self-motivated" pursuit of learning for either personal or professional reasons. Lifelong learning is important...
Learning disability, learning disorder, or learning difficulty (British English) is a condition in the brain that causes difficulties comprehending or...
transparency, and accountability; policy with clear objectives, evaluation techniques, and exit strategies; policylearning and policy experimentation; green rent...
encompasses several domains including learning theory, computer-based training, online learning, and m-learning where mobile technologies are used. The...
losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks...
and policies by ensuring they are grounded in the best available empirical evidence. This encompasses evidence-based teaching, evidence-based learning, and...
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