Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn how to optimally make decisions in a given environment. It works by estimating the value of actions taken in particular states, allowing the agent to learn from its experiences and improve its decision-making over time without needing a model of the environment. This process is key in enabling machines to make informed choices based on previous outcomes, facilitating advanced sensor fusion and decision-making strategies.
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