Gotowe bibliografie tematyczne / Action learning

Gotowa bibliografia na temat „Action learning”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Action learning"

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Koo, L. C. "Learning action learning". Journal of Workplace Learning 11, nr 3 (maj 1999): 89–94. http://dx.doi.org/10.1108/13665629910264244.

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Rimanoczy, Isabel, i Carole Brown. "Bringing Action Reflection Learning into action learning". Action Learning: Research and Practice 5, nr 2 (lipiec 2008): 185–92. http://dx.doi.org/10.1080/14767330802185889.

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Grzybowski, Anne. "Action learning in action". Perspectives: Policy and Practice in Higher Education 12, nr 4 (październik 2008): 110–16. http://dx.doi.org/10.1080/13603100802376592.

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Pealtie, Ken. "Action Learning in Action". Management Learning 27, nr 1 (marzec 1996): 87–112. http://dx.doi.org/10.1177/1350507696271006.

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Bunning, Richard L. "Action Learning". Executive Development 7, nr 4 (sierpień 1994): 3–6. http://dx.doi.org/10.1108/09533239410061770.

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Revans, Reg. "Action learning". OR Insight 2, nr 1 (styczeń 1989): 22–23. http://dx.doi.org/10.1057/ori.1989.8.

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Bourner, Tom, Liz Beaty, John Lawson i Suzanne O’Hara. "Action learning comes of age: questioning action learning". Education + Training 38, nr 8 (listopad 1996): 32–35. http://dx.doi.org/10.1108/00400919610146306.

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Pedler, Mike, i Sonja Antell. "Action learning for social action". Action Learning: Research and Practice 16, nr 1 (2.01.2019): 3–4. http://dx.doi.org/10.1080/14767333.2019.1563326.

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Wade, Sian, i Marilyn Hammick. "Action Learning Circles: action learning in theory and practice". Teaching in Higher Education 4, nr 2 (kwiecień 1999): 163–78. http://dx.doi.org/10.1080/1356251990040202.

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Rimanoczy, Isabel. "Action learning and action reflection learning: are they different?" Industrial and Commercial Training 39, nr 5 (17.07.2007): 246–56. http://dx.doi.org/10.1108/00197850710761936.

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Rozprawy doktorskie na temat "Action learning"

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Ball, Dianne Lesley School of Industrial Relations &amp Organisational Behaviour UNSW. "Facilitation of action learning groups: an action research investigation". Awarded by:University of New South Wales. School of Industrial Relations & Organisational Behaviour, 2004. http://handle.unsw.edu.au/1959.4/23407.

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The aim of this thesis is to better understand the role of the facilitator in action learning groups. In particular, it focuses on groups established within an organisation, in which the facilitator is a member of the organisation. The two central research questions are: (1) How did the facilitator influence problem solving and group interactions, and how did this vary over time and between each group, and (2) How did the facilitator's role in the organisation impact on the action learning groups? The methodology of action research and a number of principles of grounded theory are employed. The investigation was conducted within a large public teaching hospital over a two year period. Four groups volunteered to participate in the project. Two of these groups were already established and two were newly created for the purpose of the research project. The groups came from different departments and members represented a range of professional backgrounds. The size of the groups ranged from 5 to 12 participants. Each group identified a real and significant project to work through using an action learning approach. The researcher negotiated with each group what it wanted from a facilitator role, and then facilitated each meeting. All group meetings and individual interviews were audiotaped and the facilitator kept journal notes after each meeting or interview. Two potential methodological issues arose. The first related to the application of some of the principles of grounded theory to the action research investigation. The second was that the study was conducted in the researcher???s own organisation while the researcher was employed full-time, and this posed particular issues. Literature related to action learning, process consultation and small group facilitation was explored in the literature review. The purpose of this review was to critically evaluate different perspectives and approaches by frequently cited authors in these subjects, and to understand the uses and limitations of existing models. This gave the researcher an understanding of gaps in the literature related to (1) the role of the facilitator of action learning groups, and (2) conducting research in one???s organisation. Data were analysed for each group separately and then compared and contrasted in the final chapter. The objectives of the analysis were to (1) examine how the findings for each individual group address the research questions, (2) explore how the findings in each group change over time, and (2) examine how and why the findings in the groups were similar and different to each other. The findings across each of the groups have similarities and differences. There were seven interventions used by the facilitator that were common across the groups. The interventions changed over the duration of the project. Process skills were required to different degrees and at different times. Nine hypotheses were developed as the theory. Some key findings are as follows. First, it was found that groups that have not had prior experience in action learning do not understand the concept and process of facilitation and are unable to articulate in advance what they want from the facilitator. Second, the role of the facilitator cannot be separated from the skills, values and understandings of the individual facilitator. Third, a major role of the facilitator in this investigation was sharing knowledge of the organisation, the broader health care sector, and general management. In each group the facilitator performed both process and content roles, and a further role that can be called ????????????contextualising???. The findings show there is a distinction between the theoretical role and the role of an individual facilitator in practice. Further research opportunities are identified. These include (1) understanding how participants who have been involved in a facilitated action learning group may be able to apply their experience in a non-facilitated action learning group; (2) comparing the needs and expectations of participants in a facilitated action learning group within an organisation with action learning participants who are not part of an organisation; (3) understanding how facilitation of an action learning group within an organisation may change if the facilitator is in a management role, or in a peer position with participants.
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Pieropan, Alessandro. "Action Recognition for Robot Learning". Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165680.

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This thesis builds on the observation that robots cannot be programmed to handle any possible situation in the world. Like humans, they need mechanisms to deal with previously unseen situations and unknown objects. One of the skills humans rely on to deal with the unknown is the ability to learn by observing others. This thesis addresses the challenge of enabling a robot to learn from a human instructor. In particular, it is focused on objects. How can a robot find previously unseen objects? How can it track the object with its gaze? How can the object be employed in activities? Throughout this thesis, these questions are addressed with the end goal of allowing a robot to observe a human instructor and learn how to perform an activity. The robot is assumed to know very little about the world and it is supposed to discover objects autonomously. Given a visual input, object hypotheses are formulated by leveraging on common contextual knowledge often used by humans (e.g. gravity, compactness, convexity). Moreover, unknown objects are tracked and their appearance is updated over time since only a small fraction of the object is visible from the robot initially. Finally, object functionality is inferred by looking how the human instructor is manipulating objects and how objects are used in relation to others. All the methods included in this thesis have been evaluated on datasets that are publicly available or that we collected, showing the importance of these learning abilities.

QC 20150504

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Palasek, Petar. "Action recognition using deep learning". Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30828.

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In this thesis we study deep learning architectures for the problem of human action recognition in image sequences, i.e. the problem of automatically recognizing what people are doing in a given video. As unlabeled video data is easily accessible these days, we first explore models that can learn meaningful representations of sequences without actually having to know what is happening in the sequences at hand. More specifically, we first explore the convolutional restricted Boltzmann machine (RBM) and show how a stack of convolutional RBMs can be used to learn and extract features from sequences in an unsupervised way. Using the classical Fisher vector pipeline to encode the extracted features we apply them on the task of action classification. We move on to feature extraction using larger, deep convolutional neural networks and propose a novel architecture which expresses the processing steps of the classical Fisher vector pipeline as network layers. By contrast to other methods where these steps are performed consecutively and the corresponding parameters are learned in an unsupervised manner, defining them as a single neural network allows us to refine the whole model discriminatively in an end to end fashion. We show that our method achieves significant improvements in comparison to the classical Fisher vector extraction chain and results in a comparable performance to other convolutional networks, while largely reducing the number of required trainable parameters. Finally, we explore how the proposed architecture can be modified into a hybrid network that combines the benefits of both unsupervised and supervised training methods, resulting in a model that learns a semi-supervised Fisher vector descriptor of the input data. We evaluate the proposed model at image classification and action recognition problems and show how the model's classification performance improves as the amount of unlabeled data increases during training.
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Scott, Fiona Marie. "Action-reflection-learning in a lean production environment /". St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17167.pdf.

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Booth, Ian M. "Developing community dentistry through action learning : the actions, reflections and learning of a clinical director". Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520729.

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Action learning is increasingly being adopted by NHS organisations as a way of providing leadership and organisational development for staff, including senior clinicians. The benefit of action learning is that it allows work-based learning to take place with the intention of improving patient care and developing a strategic direction for individual services. Through this approach, senior clinicians will increase their selfawareness and be sensitive to the cultures within their organisation and ensure that their actions are responsive to these and not consider their actions in isolation. The research had an organisational project to consider how a salaried primary dental care service had to change in order to meet the needs of the local population in a changing NHS environment and a personal project of a journey of learning, development and change. The thesis analysed the development of the Community Dental Service from the School Dental Service to the recent developments of salaried personal dental services, including dental access centres. Moreover, it looked at how market forces and shortages in the dental workforce have influenced national policies and how these in turn have affected salaried dental services. Using my work within the Community Dental Service, I described the personal, service and organisational problems facing a clinical director. With the involvement of an action learning set, I explained how possible solutions to these problems were identified, action implemented to address them, their effects monitored and evaluated and to reflect upon the learning from these actions. The key achievements have been on a personal, professional and organisational level. I have developed as a clinical leader of a NHS service. Through the knowledge and understanding I have gained, I have matured as a manager and have been able to identify a strategy within my organisation and have implemented this for the benefit of the staff, patients and PCT. This in turn gave me personal confidence both in my work life as Clinical Director and in my life outside my work. The clinical service I lead still needs to improve, but this will always be the case. This thesis has been an opportunity for me to undertake a postgraduate qualification within a full time position in the NHS. The benefit has been the pragmatic learning which has occurred within my workplace, the learning set and through my academic studies. In summary, this process of enquiry has brought together theory, professional practice and organisational development. It is recommended that action learning is an appropriate methodology for enabling change to take place in the NHS. Action learning has been demonstrated to be a valuable approach for continuing professional development. It should be explored further within dentistry and in the wider area of health care, incorporating it into clinical leadership training programmes. 10
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Gammulle, Pranali Harshala. "Deep learning for human action understanding". Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/135199/1/Pranali_Gammulle_Thesis.pdf.

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This thesis addresses the problem of understanding human behaviour in videos in multiple problem settings including, recognition, segmentation, and prediction. Considering the complex nature of human behaviour, we propose to capture both short-term and long-term context in the given videos and propose novel multitask learning-based approaches to solve the action prediction task, as well as an adversarially-trained approach to action recognition. We demonstrate the efficacy of these techniques by applying them to multiple real-world human behaviour understanding settings including, security surveillance, sports action recognition, group activity recognition and recognition of cooking activities.
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Humphrys, Mark. "Action selection methods using reinforcement learning". Thesis, University of Cambridge, 1996. https://www.repository.cam.ac.uk/handle/1810/252269.

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The Action Selection problem is the problem of run-time choice between conflicting and heterogeneous goals, a central problem in the simulation of whole creatures (as opposed to the solution of isolated uninterrupted tasks). This thesis argues that Reinforcement Learning has been overlooked in the solution of the Action Selection problem. Considering a decentralised model of mind, with internal tension and competition between selfish behaviors, this thesis introduces an algorithm called "W-learning", whereby different parts of the mind modify their behavior based on whether or not they are succeeding in getting the body to execute their actions. This thesis sets W-learning in context among the different ways of exploiting Reinforcement Learning numbers for the purposes of Action Selection. It is a 'Minimize the Worst Unhappiness' strategy. The different methods are tested and their strengths and weaknesses analysed in an artificial world.
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Howes, Andrew. "Learning task-action mappings by exploration". Thesis, Lancaster University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.331956.

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Mourao, Kira Margaret Thom. "Learning action representations using kernel perceptrons". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7717.

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Action representation is fundamental to many aspects of cognition, including language. Theories of situated cognition suggest that the form of such representation is distinctively determined by grounding in the real world. This thesis tackles the question of how to ground action representations, and proposes an approach for learning action models in noisy, partially observable domains, using deictic representations and kernel perceptrons. Agents operating in real-world settings often require domain models to support planning and decision-making. To operate effectively in the world, an agent must be able to accurately predict when its actions will be successful, and what the effects of its actions will be. Only when a reliable action model is acquired can the agent usefully combine sequences of actions into plans, in order to achieve wider goals. However, learning the dynamics of a domain can be a challenging problem: agents’ observations may be noisy, or incomplete; actions may be non-deterministic; the world itself may be noisy; or the world may contain many objects and relations which are irrelevant. In this thesis, I first show that voted perceptrons, equipped with the DNF family of kernels, easily learn action models in STRIPS domains, even when subject to noise and partial observability. Key to the learning process is, firstly, the implicit exploration of the space of conjunctions of possible fluents (the space of potential action preconditions) enabled by the DNF kernels; secondly, the identification of objects playing similar roles in different states, enabled by a simple deictic representation; and lastly, the use of an attribute-value representation for world states. Next, I extend the model to more complex domains by generalising both the kernel and the deictic representation to a relational setting, where world states are represented as graphs. Finally, I propose a method to extract STRIPS-like rules from the learnt models. I give preliminary results for STRIPS domains and discuss how the method can be extended to more complex domains. As such, the model is both appropriate for learning data generated by robot explorations as well as suitable for use by automated planning systems. This combination is essential for the development of autonomous agents which can learn action models from their environment and use them to generate successful plans.
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Stefic, Daria. "Learning saliency for human action recognition". Thesis, Queen Mary, University of London, 2016. http://qmro.qmul.ac.uk/xmlui/handle/123456789/23656.

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When we are looking at a visual stimuli, there are certain areas that stand out from the neighbouring areas and immediately grab our attention. A map that identi- es such areas is called a visual saliency map. As humans can easily recognize actions when watching videos, having their saliency maps available might be bene cial for a fully automated action recognition system. In this thesis we look into ways of learning to predict the visual saliency and how to use the learned saliency for action recognition. In the rst phase, as opposed to the approaches that use manually designed fea- tures for saliency prediction, we propose few multilayer architectures for learning saliency features. First, we learn rst layer features in a two layer architecture using an unsupervised learning algorithm. Second, we learn second layer features in a two layer architecture using a supervision from recorded human gaze xations. Third, we use a deep architecture that learns features at all layers using only supervision from recorded human gaze xations. We show that the saliency prediction results we obtain are better than those obtained by approaches that use manually designed features. We also show that using a supervision on higher levels yields better saliency prediction results, i.e. the second approach outperforms the rst, and the third outperforms the second. In the second phase we focus on how saliency can be used to localize areas that will be used for action classi cation. In contrast to the manually designed action features, such as HOG/HOF, we learn the features using a fully supervised deep learning architecture. We show that our features in combination with the predicted saliency (from the rst phase) outperform manually designed features. We further develop an SVM framework that uses the predicted saliency and learned action features to both localize (in terms of bounding boxes) and classify the actions. We use saliency prediction as an additional cost in the SVM training and testing procedure when inferring the bounding box locations. We show that the approach in which saliency cost is added yields better action recognition results than the approach in which the cost is not added. The improvement is larger when the cost is added both in training and testing, rather than just in testing.
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Książki na temat "Action learning"

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McDowell, Liz. Action learning. Newcastle upon Tyne: MARCET, University of Northumbria at Newcastle, 1993.

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Beaty, Liz. Action learning. York: Learning and Teaching Support Network (LTSN), 2003.

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Wojciech, Gasparski, i Botham David, red. Action learning. New Brunswick (U.S.A.): Transaction Publishers, 1998.

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Boshyk, Yury, i Robert L. Dilworth, red. Action Learning. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734.

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O'Connell, Sue. From action research to action learning. Dublin: University College Dublin, 1989.

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O'Neil, Judy. Understanding action learning. New York: American Management Association, 2007.

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Boshyk, Yury, red. Action Learning Worldwide. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9781403920249.

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Joan, Feather, i Butler-Jones David, red. Building health promotion capacity: Action for learning, learning from action. Vancouver: UBC Press, 2005.

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Vinten, Gerald. Action learning internal auditing. London: City University Business School, 1992.

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Burnard, Philip. Experiential learning in action. Aldershot, Hants, England: Avebury, 1991.

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Części książek na temat "Action learning"

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Rigg, Clare, i Kiran Trehan. "Action Learning". W Encyclopedia of the Sciences of Learning, 62–66. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_824.

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Serrat, Olivier. "Action Learning". W Knowledge Solutions, 589–94. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0983-9_62.

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Park, Yongho. "Action Learning". W The Encyclopedia of Human Resource Management, 13–16. San Francisco, CA: Pfeiffer: A Wiley Imprint, 2012. http://dx.doi.org/10.1002/9781118364741.ch3.

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Dilworth, Robert L. "Explaining Traditional Action Learning: Concepts and Beliefs". W Action Learning, 3–28. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_1.

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Barker, Albert E. "Remembering Reg Revans: Action Learning’s Principal Pioneer". W Action Learning, 29–47. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_2.

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Boshyk, Yury, Albert E. Barker i Robert L. Dilworth. "Reg Revans: Sources of Inspiration, Practice, and Theory". W Action Learning, 48–72. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_3.

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Botham, David, Robert L. Dilworth i Yury Boshyk. "Revans: The Man and His Legacy". W Action Learning, 73–95. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_4.

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Dilworth, Robert L., David Bellon i Yury Boshyk. "National Level Experiments with Action Learning: Belgium and Beyond". W Action Learning, 96–113. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_5.

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Boshyk, Yury, Albert E. Barker i Robert L. Dilworth. "Milestones in the History and Worldwide Evolution of Action Learning". W Action Learning, 117–204. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_6.

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Dilworth, Robert L., i Yury Boshyk. "Action Learning in Different National and Organizational Contexts and Cultures". W Action Learning, 205–33. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230250734_7.

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Streszczenia konferencji na temat "Action learning"

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Chen, Lei, Muheng Li, Yueqi Duan, Jie Zhou i Jiwen Lu. "Uncertainty-Aware Representation Learning for Action Segmentation". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/115.

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In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action periods by uncertainty learning. Specially, we model every frame of actions with an active distribution that represents the probabilities of different actions, which captures the uncertainty of the action and exploits the tendency during the action. We evaluate our method on three popular action prediction datasets: Breakfast, Georgia Tech Egocentric Activities (GTEA), and 50Salads. The experimental results demonstrate that our method achieves the performance with state-of-the-art.
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Lee, Dongha, Chanyoung Park, Hyunjun Ju, Junyoung Hwang i Hwanjo Yu. "Action Space Learning for Heterogeneous User Behavior Prediction". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/392.

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Users' behaviors observed in many web-based applications are usually heterogeneous, so modeling their behaviors considering the interplay among multiple types of actions is important. However, recent collaborative filtering (CF) methods based on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces: 1) action space which captures the relations among all observed and unobserved actions, and 2) entity space which captures high-level similarities among users and among items. Each action vector in the action space is computed using a non-linear function and its corresponding entity vectors in the entity space. In addition, METAS adopts an efficient triplet mining algorithm to effectively speed up the convergence of metric learning. Experimental results show that METAS outperforms the state-of-the-art methods in predicting users' heterogeneous actions, and its entity space represents the user-user and item-item similarities more clearly than the space trained by the other methods.
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Yonemoto, Satoshi. "GA-based Action Learning". W 7th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005613902930298.

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Buraglia, Gabriela, Yerika Jimenez i Christina Gardner-McCune. "Learning Trajectories in Action". W SIGCSE '20: The 51st ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3328778.3372659.

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Brailas, Alexios. "Rhizomatic Learning in Action". W TEEM'20: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3434780.3436565.

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Hirsch, Benjamin, George W. Hitt, Leigh Powell, Kinda Khalaf i Shadi Balawi. "Collaborative learning in action". W 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE). IEEE, 2013. http://dx.doi.org/10.1109/tale.2013.6654447.

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Feng, Wenfeng, Hankz Hankui Zhuo i Subbarao Kambhampati. "Extracting Action Sequences from Texts Based on Deep Reinforcement Learning". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/565.

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Extracting action sequences from texts is challenging, as it requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require either the set of candidate actions be provided in advance, or action descriptions are restricted to a specific form, e.g., description templates. In this paper we aim to extract action sequences from texts in \emph{free} natural language, i.e., without any restricted templates, provided the set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view ``selecting'' or ``eliminating'' words from texts as ``actions'', and texts associated with actions as ``states''. We build Q-networks to learn policies of extracting actions and extract plans from the labeled texts. We demonstrate the effectiveness of our approach on several datasets with comparison to state-of-the-art approaches.
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Li, Jun, i Sinisa Todorovic. "Action Shuffle Alternating Learning for Unsupervised Action Segmentation". W 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01244.

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Fan, Zhou, Rui Su, Weinan Zhang i Yong Yu. "Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/316.

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In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning.
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Tsutsui, Satoshi, Xizi Wang, Guangyuan Weng, Yayun Zhang, David J. Crandall i Chen Yu. "Action Recognition based on Cross-Situational Action-object Statistics". W 2022 IEEE International Conference on Development and Learning (ICDL). IEEE, 2022. http://dx.doi.org/10.1109/icdl53763.2022.9962199.

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Raporty organizacyjne na temat "Action learning"

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Fitzpatrick, Paul M., Giorgio Metta, Lorenzo Natale, Sajit Rao i Giulio Sandini. Learning about Objects through Action - Initial Steps towards Artificial Cognition. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2001. http://dx.doi.org/10.21236/ada434778.

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Lewis, Larry. Improving Lethal Action: Learning and Adapting in U.S. Counterterrorism Operations. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2014. http://dx.doi.org/10.21236/ada610608.

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Hardie, Tom, Tim Horton, Nell Thornton-Lee, Joe Home i Penny Pereira. Developing learning health systems in the UK: Priorities for action. The Health Foundation, wrzesień 2022. http://dx.doi.org/10.37829/hf-2022-i06.

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Whitbeck, Barbara. Strengths in Action: Implementing a Learning Organization Model in a Human Service Setting. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.2093.

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Pritchett, Lant, Kirsty Newman i Jason Silberstein. Focus to Flourish: Five Actions to Accelerate Progress in Learning. Research on Improving Systems of Education (RISE), grudzień 2022. http://dx.doi.org/10.35489/bsg-rise-misc_2022/07.

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There is a severe global learning crisis. While nearly all children start school, far too many do not learn even the most foundational skills of reading, writing, and basic mathematics during the years they spend there. The urgent need to address this crisis requires no elaborate reasoning. If one starts with love for a child, a human universal, it is easy to see that in the modern world a child’s dignity, self-worth, and freedom to define their own destiny require an adequate education. An adequate education is what will then enable that child to lead a full adult life as a parent, community member, citizen, and worker in the 21st century. To enable every child to leave school with the foundational skills they need will require fundamental changes to education systems. Since 2015, the Research on Improving Systems of Education (RISE) Programme, with which we are affiliated, has been conducting research exploring how to make these changes through country research teams in seven countries (Ethiopia, India, Indonesia, Nigeria, Pakistan, Tanzania, and Vietnam) and crosscutting teams on the political economy of education reform. Drawing on the cumulative body of research on learning outcomes and systems of education in the developing world, both from the RISE Programme and other sources, we advocate for five key actions to drive system transformation. (See next page.) A message cutting across all five actions is “focus to flourish”. Education systems have been tremendously successful at achieving specific educational goals, such as expanding schooling, because that is what they committed to, that is what they measured, that is what they were aligned for, and that is what they supported. In order to achieve system transformation for learning, systems must focus on learning and then act accordingly. Only after a system prioritises learning from among myriad competing educational goals can it dedicate the tremendous energies necessary to succeed at improving learning. The research points to these five actions as a means to chart a path out of the learning crisis and toward a future that offers foundational skills to all children. The first section that follows provides background on the depth and nature of the learning crisis. The remainder of the document explains each of the five actions in turn, synthesising the research that informs each action, contrasting that action with the prevailing status quo, and describing what the action would entail in practice.
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Bhattacharjea, Suman, Sehar Saeed, Rajib Timalsina i Syeed Ahamed. Citizen-led Assessments: A Model for Evidence-based Advocacy and Action to Improve Learning. Australian Council for Educational Research, czerwiec 2021. http://dx.doi.org/10.37517/978-1-74286-636-9.

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Designed as household-based assessments, citizen-led assessments (CLAs) are implemented by local organizations who assess children in their homes, thus reaching the most marginalized children, families, and communities, often in remote areas. CLAs add an essential piece of information for truly monitoring progress and help realistically represent the learning levels of all children – at national, regional, and global levels. By using simple tools and easy-to-understand reports, CLAs engage parents and community members in discussions about learning and help foster understanding of the importance of ensuring quality education through civil action. In this publication, members from organizations conducting CLAs in India, Pakistan, Nepal and Bangladesh provide an overview of the CLA model and illustrate a range of ways in which the model has been implemented in the four South Asian countries to monitor and improve learning. In all four countries, the initiative is known as the Annual Status of Education Report, or ASER – a word that means ‘impact’ in three of these four countries. By design, ASER assesses foundational reading and numeracy skills.
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House, Sarah. Learning in the Sanitation and Hygiene Sector. Institute of Development Studies (IDS), lipiec 2021. http://dx.doi.org/10.19088/slh.2021.004.

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This SLH Learning Paper summarises the key learning from a rapid topic exploration on 'Learning in the Sanitation and Hygiene Sector'. The study looked at how people in the WASH sector learn, the processes utilised and what works best, as well as the barriers and challenges to learning. It looks at learning from communities and peer-to-peer and how the learning gets translated into action at scale. How do you think we learn best? What barriers do you see and experience that make it more difficult for us to learn? And what steps should be taking to reduce the barriers and improve how to learn more effectively? This paper shares the lessons from sector and associated actors working in low- and middle-income contexts around the world and makes recommendation on how to strengthen learning and sharing processes, as well as building capacities and confidence for learning, with the ultimate aim of turning that learning into action at scale.
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Kaffenberger, Michelle, Jason Silberstein i Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), kwiecień 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.

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While conventional interventions and evaluations address the symptoms of the learning crisis, there is growing acknowledgement that widespread and sustained learning improvements will require systems approaches that diagnose and address the root causes of low learning. This paper presents and applies three methods to evaluate education systems and inform how to improve system coherence for learning. First, we use learning trajectories to evaluate the dynamics of children’s learning in 22 low- and middle-income countries. Second, we present a set of principles called the ALIGNS principles and show how they can be used to evaluate and improve alignment of curricula, assessments, and teacher support and instruction. Finally, we present a systems diagnostic framework and apply it to a program in South Africa, showing how the program takes a systems approach to improve learning. These tools help concretize systems thinking and bring insights to bear on the design and evaluation of policies and programs intended to improve learning.
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Wickenden, Mary, Brigitte Rohwerder i Stephen Thompson. IDS Action Learning Groups with Jobseekers and Employers Involved in the Inclusion Works Programme. A Report on the Process and Findings. Institute of Development Studies, czerwiec 2022. http://dx.doi.org/10.19088/ids.2022.047.

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The report describes how IDS organised and ran a series of action learning groups with jobseekers and employers involved with the FCDO-funded Inclusion Works programme. Action learning groups are a participatory way of learning over time about the participants’ experiences of the programme and their wider perceptions of the world of work and moves towards more inclusive practice generally. The groups were held in the four programme countries: Bangladesh, Kenya, Nigeria and Uganda. The report includes key findings and reflections from the groups, the local facilitators and the IDS team.
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Cho, Emily EunYoung, Karen Austrian i Nicole Haberland. From Data to Action: Informing Menstrual Health Management Programs. Population Council, 2022. http://dx.doi.org/10.31899/sbsr2022.1019.

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The Population Council’s Girl Innovation, Research, and Learning (GIRL) Center and Evidence for Gender and Education Resource (EGER) program hosted the second virtual webinar of its From Data to Action series, “Informing Menstrual Health Management Programs” in February 2022. This document provides a recap of the webinar, which featured a global systematic review and results from a randomized controlled trial in Kenya on menstrual health management (MHM) programs. ZanaAfrica shared perspectives on program implementation and how the evidence is shaping their approach.
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