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AI agents learn autonomously through a process called reinforcement learning (RL), which allows them to make decisions by interacting with an environment. The agent takes actions and receives feedback in the form of rewards or penalties, based on the outcome of its actions. This feedback helps the agent adjust its behavior over time to maximize rewards and achieve specific goals. The learning process can be supervised, semi-supervised, or unsupervised, depending on the setup. In some cases, deep learning models are used to allow AI agents to handle complex tasks by learning from vast amounts of data. Reinforcement learning is particularly useful in environments where the correct actions are not predefined and must be discovered by the agent through trial and error. The more an AI agent interacts with its environment, the better it can fine-tune its decision-making capabilities. Popular frameworks like OpenAI’s Gym and Google’s TensorFlow can be used to build and train AI agents for various tasks.
SOURCE: https://www.inoru.com/ai-agent-development-company
SOURCE: https://www.inoru.com/ai-agent-development-company