Example Concept: In reinforcement learning for path planning, a robot is treated as an agent that interacts with its environment. The environment is dynamic, meaning it can change unpredictably. The agent receives feedback in the form of rewards based on its actions, such as moving towards a goal or avoiding obstacles. Over time, the agent learns a policy, which is a strategy that maps states (e.g., robot's position and environment conditions) to actions (e.g., move forward, turn left). The goal of RL is to find a policy that maximizes cumulative rewards, thereby optimizing the robot's path planning in dynamic settings.