Description: Reward maximization is a fundamental concept in reinforcement learning, an area of artificial intelligence that focuses on how agents can learn to make decisions through interaction with an environment. In this context, an agent seeks to select actions that allow it to accumulate the highest possible rewards over time. This process involves exploring different actions and exploiting those that have proven to be more effective in the past. Reward maximization is based on the idea that the decisions made by the agent should be aimed at optimizing its performance, which translates into obtaining the greatest cumulative reward. This approach is crucial for the development of algorithms that can learn from experience, adapting to changing situations and improving their performance over time. Reward maximization applies not only to simple problems but also extends to complex scenarios such as games, robotics, and various decision-making systems, where effective decision-making is essential to achieving desired goals. In summary, reward maximization is a central principle that guides the behavior of agents in reinforcement learning, enabling them to learn and adapt to their environment efficiently.
