Total reward意思

"Total reward" is a term used in reinforcement learning, which is a branch of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In this context, the total reward refers to the sum of all immediate rewards that an agent receives over the course of an episode or a sequence of actions.

For example, consider a simple game where an agent (e.g., a robot or a software program) is trying to navigate through a maze to find a treasure. At each step, the agent can choose to move in one of four directions (up, down, left, right). If the agent chooses a direction that leads to an increase in its score (e.g., by moving closer to the treasure), it receives a positive reward. If it chooses a direction that leads to a decrease in its score (e.g., by moving away from the treasure or hitting a dead end), it receives a negative reward. The goal of the agent is to learn a policy that maximizes its total reward, which is the sum of all the rewards it receives from the beginning to the end of the episode (when it finds the treasure or reaches a terminal state).

In reinforcement learning, the total reward is a critical component of the learning process. The agent uses the total reward it receives from its actions to update its policy (i.e., the strategy it uses to choose actions) and improve its performance over time. The objective is to find a policy that maximizes the expected value of the total reward, which is known as the value function.