Reinforcement learning can be seen as a simulation of how humans learn from experience. Just as a child learns to navigate the world by trial and error, reinforcement learning enables AI agents to learn through interaction with an environment.
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Reinforcement learning (RL) is an area of machine learning that focuses on how an agent takes actions within an environment in order to maximize a reward. This type of learning involves trial and error, with the agent receiving positive reinforcement when it performs a desired behavior, and negative reinforcement when it doesn’t. In this way, the agent is able to learn from its mistakes and adjust its behavior accordingly.
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