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They can be used to acquire the optimal value function and/or the optimal policy.
Most notably here Value-Iteration and Policy-Iteration are being used, both of which have their origins in the field of Dynamic Programming (Bellmann 1957) and are, strictly-speaking, therefore not RL algorithms (see Kaelbling et al 1996 for a discussion).
Note, the neuronal perspective of RL is in general indeed meant to address biological questions.
Its goals are usually not related to those of other artificial neural network (ANN) approaches (this is addressed by the machine-learning approach of RL).
An RL agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration), which is essentially trial and error learning.
The reinforcement signal that the RL-agent receives is a numerical reward, which encodes the success of an action's outcome, and the agent seeks to learn to select actions that maximize the accumulated reward over time.
If the model (T and r) of the process is not known in advance, then we are truly in the domain of RL, where by an adaptive process the optimal value function and/or the optimal policy will have to be learned.
The most influential algorithms, which will be described below, are: Early on, we note that the state-action space formalism used in reinforcement learning (RL) can be also translated into an equivalent neuronal network formalism, as will be discussed below.
Furthermore RL is necessarily linked to biophysics and the theory of synaptic plasticity.
RL methods are used in a wide range of applications, mostly in academic research but also in fewer cases in industry.