Reinforcement Learning (RL) is a learning paradigm in which an agent learns an optimal strategy through interaction and feedback in the form of rewards within an environment.
The agent observes the current state, selects an action, and then receives a reward as well as the next state.
The goal is to find a policy that maximizes the long-term cumulative reward.
Important concepts are Markov Decision Processes (MDP), value functions, and Q-learning.
RL is used in areas such as robotics, games, and autonomous systems, where learning through trial and error is possible.