Example Concept: In reinforcement learning, an agent interacts with an environment in discrete time steps. At each step, the agent receives a state from the environment, selects an action based on a policy, and receives a reward. The goal is to learn a policy that maximizes the cumulative reward over time. The agent updates its policy based on the rewards received, often using techniques like Q-learning or policy gradients.