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Etymologie

reinforce$68618$ - Übersetzung nach griechisch

FIELD OF MACHINE LEARNING

Reinforcement Learning; Actor critic architecture; Actor critic model; Reinforcement Learning a form of Artificial Intelligence; Reward function; Inverse reinforcement learning; Learning from demonstration; Policy gradient method; Actor–critic method; Actor-critic method; REINFORCE algorithm; Actor critic; Direct policy search; Algorithms for control learning; DDPG; Deep deterministic policy gradient; RL agent; Reinforced learning; List of reinforcement learning algorithms; Partially supervised reinforcement learning; Associative reinforcement learning; Safe reinforcement learning

reinforce

v. ενισχύω

Definition

Reinforce

·noun ·see Reenforce, ·noun.

II. Reinforce ·vt ·see Reenforce, ·vt.

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Wikipedia

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

https://en.wikipedia.org/wiki/Reinforcement learning