REWARD maximization is a technique used in Reinforcement LEARNING. Reinforcement learning is a subset of AI algorithms made up of three main components: an ENVIRONMENT, agents, and incentives. The agent alters its own and the environment's state by completing actions. The agent is awarded or penalized based on how much their activities affect the goal the agent must attain. Many reinforcement learning challenges begin with the agent having no prior knowledge of the environment and conducting random behaviors. The agent learns to optimize its actions and adopt policies that maximize its reward based on the feedback it receives.

The goal is to maximize the reward and the action of the agent by using optimal policies. This is called reward maximization. Any abilities that are repeatedly requested by the agent's environment must eventually be created in the agent's behavior if it can alter it to improve its cumulative reward. A good reinforcement learning agent could eventually learn perception, language, social INTELLIGENCE, and other skills while maximizing its reward.