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Reinforcement learning
Reinforcement learning

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!!! Overview
[{$pagename}] is [learning] that is [Supervised Learning] and typically associated with [Machine Learning]
In [{$pagename}] the goal is to develop a system (agent) that improves its performance based on interactions with the environment. Since the information about the current state of the environment typically also includes a so-called [reward signal|reward function], we can think of [{$pagename}] as a field related to [Supervised Learning].
However, in [{$pagename}] this [feedback] is not the correct [ground truth] label or value, but a measure of how well the action was measured by a [reward function]. Through its interaction with the environment, an agent can then use [{$pagename}] to learn a series of actions that maximizes this reward via an exploratory trial-and-error approach or deliberative planning.
!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop