+Reinforcement Learning (RL) is a paradigm where an [Agent](/wiki/agent) learns optimal behaviors by interacting with an [Environment](/wiki/environment). Through trial and error, it discovers which actions maximize a cumulative reward signal, much like learning from experience. This iterative process allows the agent to make intelligent decisions over time.
+## See also
+- [Machine Learning](/wiki/machine_learning)
+- [Deep Learning](/wiki/deep_learning)
+- [Artificial Intelligence](/wiki/artificial_intelligence)
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