Agent Training

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Agent training usually takes place in a reinforcement learning setting, where an AI agent learns through trial and error. The agent observes what is happening, takes an action, and receives a reward that reflects how helpful or harmful that action was. By repeating this process many times, it adjusts its internal rules for choosing actions so it earns higher rewards in the long run.

Training can happen in a simulator or in the real world. Simulators are often used because they allow the agent to make mistakes without real-world consequences. During training, the agent must balance trying new actions with using what it already knows. The way the training process is configured, for example, how quickly the agent updates its knowledge or how far into the future it values rewards, has a strong effect on the final result.

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