What is considered a reward in reinforcement learning?

In reinforcement learning, a reward is defined as the feedback received after an agent takes an action within an environment. This feedback serves to inform the agent about the effectiveness of its action in achieving its goals. Rewards can be either positive or negative, reinforcing behavior that leads to desirable outcomes and discouraging actions that result in unfavorable consequences. This mechanism enables the agent to learn over time which actions yield the best results in various situations, driving the learning process.

The initial conditions of the task, while important for setting up the problem, do not constitute a reward; they merely provide context for the learning environment. Penalties faced by the agent can be a form of feedback, but they focus more on negative outcomes rather than the broader concept of reward, which encompasses both positive rewards and penalties. Finally, the rules governing the environment establish how the agent interacts with that environment but do not directly represent the feedback received from those interactions. Therefore, the feedback received after an action is taken is correctly identified as the reward in reinforcement learning.