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Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
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Rapid learning (or Rapid eLearning Development) has traditionally referred to a methodology to build e-learning courses rapidly. [1] Typically the author will create slides in PowerPoint, record audio and video narration on top of the slides, and then use software to add tests, or even collaboration activities between the slides.
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For presentations in other languages, see the interlanguage links. Please add your presentation also to meta:Presentations , the international master list on Meta-Wiki. User:Jimbo Wales/BBC talk : An outline of the presentation Jimbo and Angela did in London (posted 2004)
A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs that utilize CNNs can learn directly from high-dimensional sensory inputs via reinforcement learning.
iSpring Suite is a PowerPoint-based authoring toolkit produced by iSpring Solutions that allows users to create slide-based courses, quizzes, dialog simulations, screencasts, video lectures, and other interactive learning materials.
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