Search results
Results from the WOW.Com Content Network
Osher Lifelong Learning Institutes (OLLI) offer noncredit courses with no assignments or grades to adults over age 50. Since 2001, philanthropist Bernard Osher has made grants from the Bernard Osher Foundation to launch OLLI programs at 120 universities and colleges throughout the United States.
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Lifelong learning institutes use two fundamentally-different meeting styles: instructor-led and peer-led. The meeting style can affect many aspects of the learning and social experience in a lifelong learning institute. Instructor-led meetings use an expert lecturer to present content to a passive group of lifelong learning institute members ...
Text taken from 3rd global report on adult learning and education: the impact of adult learning and education on health and well-being, employment and the labour market, and social, civic and community life , 19, UNESCO, UNESCO. UNESCO. This article incorporates text from a free content work. Licensed under CC BY-SA 3.0 IGO.
Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.
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.
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.