👉 Reward computing is an approach within the field of machine learning and artificial intelligence that focuses on training models through reinforcement learning by providing feedback in the form of rewards or penalties based on the model's actions. Unlike traditional supervised learning, where models are trained using labeled data, reward computing allows agents to learn optimal behaviors by receiving rewards for desirable actions and penalties for undesirable ones. This method is particularly useful in complex, dynamic environments where explicit instructions are difficult to define, such as robotics, game playing, and autonomous systems. By iteratively adjusting actions based on the received rewards, models can develop strategies that maximize cumulative rewards over time, leading to more adaptive and intelligent behavior.
