Reinforcement learning is a machine learning technique in which an agent learns to make decisions in an interactive environment through the feedback it receives from its action. The agent's goal is to maximise a long-term numerical reward, which is given to it for making the correct decisions in the environment.
Reinforcement learning is based on the concept of trial and error, where the agent learns through continuous interaction with the environment, adjusting its actions according to the rewards and penalties it receives. The agent explores different actions in the environment, observes the results and learns to select the actions that maximise long-term reward.
Reinforcement learning is commonly used in robotics, gaming and process automation applications, where an autonomous agent must learn to make real-time decisions to achieve specific goals.
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