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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 ...
YouTube was founded as a video sharing platform in 2005 and is now the most visited website in the US as of 2019. [1] Almost immediately after the site's launch, educational institutions, such as MIT OpenCourseWare and TED , were using it for the distribution of their content.
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 .
In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or more of the different agents.
Many applications of reinforcement learning do not involve just a single agent, but rather a collection of agents that learn together and co-adapt. These agents may be competitive, as in many games, or cooperative as in many real-world multi-agent systems. Multi-agent reinforcement learning studies the problems introduced in this setting.
Eight years later, the ownership of the channel was privatized and its name was changed to The Learning Channel. It showcased documentaries on a variety of topics, like "Paleoworld" and "Amazing ...
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 ...
[10]: 252 The main difference is that reinforcement always increases the likelihood of a behavior (e.g., channel surfing while bored temporarily alleviated boredom; therefore, there will be more channel surfing while bored), whereas punishment decreases it (e.g., hangovers are an unpleasant stimulus, so people learn to avoid the behavior that ...