Reinforcement learning (RL) plays a critical role in training AI agents. In RL, agents learn by interacting with their environment, receiving rewards or penalties based on their actions. The goal is for the agent to maximize cumulative rewards, thereby learning optimal strategies for complex tasks. This type of learning is particularly useful in scenarios where explicit programming isn’t feasible, such as autonomous driving or robotics. In AI agent development, RL allows for real-time adjustments and continuous improvement, making it an ideal approach for building agents that can function in unpredictable or dynamic environments. Algorithms like Q-learning and deep Q-networks are commonly used for training agents through RL.