Results 41 to 50 of about 12,004,986 (327)
Breaking the cycle of teacher shortages: What kind of policies can make a difference?
Education Policy Analysis Archives, 2019 Teacher shortages have recurred in the United States over many decades. This article introduces a special issue of EPAA that seeks to better understand the factors that contribute to the insufficient supply and inequitable distribution of qualified ...
Linda Darling-Hammond, Anne Podolsky
doaj +1 more source
Machine learning could improve innovation policy [PDF]
, 2020 Accepted ...
Furman, Jeffrey, Teodoridis, Florenta
core +1 more source
European Policy Analysis, 2022 Understandings of different policy learning types have matured over recent decades. However, relatively little is known about their nonlinear and interactive nature, particularly within crisis contexts.
B. Zaki, V. Pattyn, Ellen Wayenberg
semanticscholar +1 more source
Mediation, translation and local ecologies: understanding the impact of policy levers on FE colleges [PDF]
, 2007 This article reports the views of managers and tutors on the role of policy ‘levers’ on teaching, learning, and inclusion in colleges of Further Education (FE) in our research project, ‘The impact of policy on learning and inclusion in the Learning and ...
Bathmaker A.‐M. +16 more
core +3 more sources
Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey
International Joint Conference on Artificial Intelligence, 2021 Reinforcement Learning (RL) algorithms have had tremendous success in simulated domains. These algorithms, however, often cannot be directly applied to physical systems, especially in cases where there are constraints to satisfy (e.g. to ensure safety or
Yongshuai Liu, A. Halev, Xin Liu
semanticscholar +1 more source
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
, 2020 Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or
Cheng, Yingfeng +10 more
core +1 more source
Administration & Society, 2021 Policy failures are often assumed to be unintentional and anomalous events about which well-intentioned governments can learn why they occurred and how they can be corrected. These assumptions color many of the results from contemporary studies of policy
Ching Leong, Michael Howlett
semanticscholar +1 more source
Preference-Based Policy Learning [PDF]
, 2011 Many machine learning approaches in robotics, based on re- inforcement learning, inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulator- free direct policy learning, called Preference-based Policy Learning (PPL). PPL iterates a four-step process: the robot demonstrates a candidate pol-
Akrour, Riad +2 more
openaire +2 more sources
Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation
International Joint Conference on Artificial Intelligence, 2021 Sequential recommendation aims to predict users’ future behaviors given their historical interactions. However, due to the randomness and diversity of a user’s behaviors, not all historical items are informative to tell his/her next choice. It is obvious
Xiaohai Tong +4 more
semanticscholar +1 more source
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret [PDF]
, 2010 The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions.
Ananthram Swami +4 more
core +6 more sources