Description: Curriculum in Reinforcement Learning is a structured approach used to train artificial intelligence agents by gradually increasing the difficulty of the tasks they must perform. This method is based on the premise that agents learn from experience, receiving rewards or penalties based on their actions. By starting with simpler tasks and progressively increasing complexity in a controlled manner, the aim is to facilitate the learning process, allowing the agent to develop fundamental skills before facing more complex challenges. This approach not only enhances learning efficiency but also helps avoid overfitting and frustration that can arise from attempting to solve difficult problems from the outset. In the context of reinforcement learning, the curriculum can be adapted to various algorithms and architectures, yielding a more effective training regimen. Implementing a well-designed curriculum can result in more robust and generalizable learning, which is crucial in applications where adaptability and accuracy are essential. In summary, Curriculum in Reinforcement Learning represents a valuable strategy for improving the performance of AI agents, enabling a more effective and efficient development of their capabilities.
