Cumulative reward refers to the total amount of reward an agent accumulates over a given period while interacting with an environment in reinforcement learning. It is a critical concept in the field of artificial intelligence and machine learning, particularly in the context of training agents to make decisions that maximize long-term benefits.
In reinforcement learning, an agent learns to take actions within an environment to achieve specific goals. These goals are often represented in terms of rewards, which can be positive or negative. The cumulative reward is calculated by summing all rewards received by the agent over time, from the start of the learning process until the present moment or until a terminal state is reached.
The goal of most reinforcement learning algorithms is to maximize the cumulative reward. This is often expressed as a return, which may involve discounting future rewards to account for their present value. The discount factor, typically denoted by gamma (γ), determines how much weight is given to future rewards compared to immediate rewards. A higher discount factor means that the agent considers future rewards more significantly, while a lower factor emphasizes immediate rewards.
Understanding cumulative reward is essential for evaluating and comparing the performance of different reinforcement learning algorithms. It provides insights into how well an agent learns to navigate its environment and make decisions that lead to optimal outcomes.