Autonomous Vehicle Systems
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards. It involves trial and error, where the agent receives feedback in the form of rewards or penalties based on its actions, helping it to develop strategies over time. This learning paradigm is crucial for autonomous systems, as it enables vehicles to adapt to dynamic environments, predict behaviors of other agents, and make informed decisions.
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