Description: Dynamic optimization refers to the process of continuously adjusting strategies to optimize performance over time. In the context of reinforcement learning, this concept becomes crucial as agents must learn to make decisions in changing and often uncertain environments. Dynamic optimization involves adapting the agent’s action policies based on feedback received from the environment, allowing it to improve its performance on specific tasks. This approach is based on the idea that environmental conditions may vary, and therefore, strategies that work at one moment may not be effective later. Through exploration and exploitation, agents can discover new strategies that maximize long-term rewards. Dynamic optimization is also related to the concept of continuous learning, where the agent is not only trained on a static dataset but adapts to new experiences and changes in the environment. This process is fundamental for the development of artificial intelligence systems that can operate effectively in various real-world situations, where variability and uncertainty are the norm.
