Reinforcement Learning Algorithms are a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn which actions yield the best outcomes over time. This process is often modeled using concepts from Markov Decision Processes. Common algorithms in reinforcement learning include Q-learning and Deep Q-Networks (DQN). These algorithms help the agent to evaluate the value of different actions and improve its strategy through trial and error. As the agent explores its environment, it gradually learns to maximize its cumulative reward.