Reinforcement Learning in Machine Learning

Reinforcement learning in machine learning is a dynamic field that focuses on enabling AI systems to make sequential decisions through interaction with an environment. It involves an agent that learns to take actions to maximize a reward signal over time. By employing trial-and-error learning, reinforcement learning algorithms seek to optimize decision-making processes.