Sentence examples for model reinforcement from inspiring English sources

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The influence of several modelling parameters was investigated, including geometric nonlinearity, element properties, concrete model, reinforcement model and boundary condition.

Summary: Psychologists and neuroscientists routinely borrow ideas from machine learning to understand and model reinforcement learning in humans and animals.

For example, Social Cognitive Theory states that modelling is only effective when the participants meet certain conditions, such as identification with the model; reinforcement of the model; and the demonstration of adequate skills by the model to the participant.

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For Ray Allen and other N.B.A. players who disagreed with Charles Barkley's well-publicized sentiment that they were not role models, reinforcement has arrived.

Student creativity can be enhanced through social modelling, reinforcement, and classroom ecology.

Unlike other methods of generating AI models, reinforcement learning lends itself to be more like sci-fi than reality.

It covers research related to autonomous agents, multi-agent systems, behavioral modeling, reinforcement learning, game theory, mechanism design, machine learning, meta-heuristic search, optimization, planning and scheduling, artificial neural networks, evolutionary computation, swarm intelligence and other algorithms for intelligent systems.

A startup out of Cambridge called Prowler.io is developing a new kind of decision-making platform based on probabilistic modelling, reinforcement learning and game theory that it believes may help the AI community answer that question with a "yes".

Thus the dopamine prediction error responses occur in both model-free and model-based reinforcement learning situations, without evidence so far for a role in initial model acquisition.

Gläscher, J., Daw, N., Dayan, P. & O'Doherty, J. P. States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

Students implement model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.

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