The purpose of the reward signal in reinforcement learning (RL) is to provide the agent with immediate feedback about the quality of its actions in a given environment. This signal acts as a guide, helping the agent learn which behaviors are desirable and which should be avoided. Unlike supervised learning, where explicit labels are provided, RL relies on rewards to iteratively shape the agent’s policy—the strategy it uses to select actions. For example, in a game like chess, a reward might be +1 for winning, -1 for losing, and 0 for ongoing moves, steering the agent toward strategies that lead to victory.
The design of the reward signal is critical because it directly influences how the agent prioritizes short-term versus long-term outcomes. A well-structured reward balances immediate feedback with the agent’s need to plan ahead. For instance, in training a self-driving car, a simple reward might penalize collisions and reward forward progress. However, if the reward only values speed, the agent might learn to drive recklessly. To address this, engineers often design reward functions that include multiple factors, such as maintaining safe distances or conserving energy. Sparse rewards—like receiving a reward only upon completing a task—can also pose challenges, as the agent may struggle to connect its actions to distant outcomes. Techniques like reward shaping (adding intermediate rewards) or using intrinsic motivation (e.g., curiosity) help bridge this gap.
The reward signal also enables the agent to balance exploration (trying new actions) and exploitation (using known effective actions). For example, in a recommendation system, an RL agent might explore suggesting less-popular items to discover hidden user preferences, but it must also exploit known high-click-rate items to maintain user engagement. The discount factor (gamma) in RL algorithms further adjusts how the agent values future rewards—higher gamma prioritizes long-term gains, while lower gamma focuses on immediate results. Ultimately, the reward signal is the foundation for the agent’s learning process, determining whether it converges to a useful policy or gets stuck in suboptimal behaviors.