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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.
The Electronic Classroom of Tomorrow (ECOT) was a community/charter school based in Columbus, Ohio, United States.It was sponsored by the Lucas County Educational Service Center of Lake Erie West (ESCLEW) in Toledo, in accordance with chapter 3314 of the Ohio Revised Code.
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 ...
Academy With Community Partners, headquartered in Mesa, Arizona; Gateway Online Academy of Ohio, headquartered in Independence, Ohio; Michigan Online School, headquartered in Gobles, Michigan
The U.S. state of Ohio is home to a number of public and private institutions of higher learning. Prior to statehood, the Northwest Ordinance of 1787 included a provision to establish an institution of higher education in what became Ohio. American Western University was chartered in 1802 as a result, but never opened.
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.
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...