Reinforcement Learning
Reinforcement Learning is a type of machine learning where an agent learns to behave in an environment by performing certain actions and receiving rewards or penalties. The goal of the agent is to maximize the cumulative reward over time. The agent learns through trial and error, adjusting its actions based on the feedback it receives from the environment. Reinforcement Learning is used in various applications such as game playing, robotics, and autonomous driving. It is based on the idea of Markov Decision Processes (MDPs) and involves the use of algorithms such as Q-learning, SARSA, and Deep Reinforcement Learning.
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