👉 Feed math, often used in reinforcement learning and game theory, is a mathematical framework that models decision-making processes where an agent learns to make optimal choices by interacting with an environment. It involves defining a "reward function" that quantifies the desirability of actions taken by the agent, guiding the learning process towards maximizing cumulative rewards. The agent's strategy, or policy, is updated iteratively based on the feedback received from these rewards, typically using algorithms like Q-learning or policy gradients. This approach allows agents to adapt and improve their behavior over time, even in complex, dynamic environments, by balancing exploration (trying new actions) and exploitation (using known best actions).
