What essential concept must an RL agent learn to maximize over a sequence of interactions?
Answer
Cumulative reward.
At its core, Reinforcement Learning describes a process where an agent learns to execute sequential decisions within an environment specifically to maximize some notion of cumulative reward. The learning process is guided by receiving numerical reward signals after taking an action and transitioning to a new state. This trial-and-error mechanism is powerful precisely because it is designed for tasks where the sequence of optimal actions is not known beforehand or cannot be explicitly programmed. The agent continuously updates its policy based on the total accumulated reward over time, which dictates the long-term success of its chosen actions.
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