What is reinforcement learning and how is it used in AI?

[Christian11]

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions and receiving feedback from the environment in the form of rewards or penalties. Unlike supervised learning, where the model learns from labeled data, RL models explore their environment and learn through trial and error. RL is widely used in areas like robotics, game AI (such as AlphaGo), and autonomous vehicles. It can be used to train agents that optimize complex processes, such as managing resources or controlling systems in dynamic environments. The main components of an RL system are the agent, environment, state, action, and reward. Popular algorithms include Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO).



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