How do reinforcement learning algorithms work in AI agents?

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Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties based on its actions. The goal of the agent is to maximize cumulative rewards over time. RL involves three key components: the agent, the environment, and the reward signal. The agent observes the environment’s state, takes an action, and receives a reward or penalty. Popular RL algorithms include Q-learning, deep Q-networks (DQN), and Proximal Policy Optimization (PPO). In AI agent development, RL can be used for training agents in complex tasks, such as gaming or robotic navigation.

Source : https://www.inoru.com/ai-agent-development-company