Curriculum learning in reinforcement learning (RL) is a training strategy that involves gradually increasing the difficulty of tasks presented to the learning agent. Instead of exposing the agent to all possible scenarios at once, which can lead to confusion or poor performance, curriculum learning introduces simpler tasks first and progressively incorporates more complex challenges as the agent improves. This method mirrors how humans often learn by starting with foundational concepts before tackling more advanced topics.
For instance, consider a robot learning to navigate a maze. Instead of placing the robot directly in a complex maze, curriculum learning would start with a simple straight path or an open space. Once the robot successfully completes these easier tasks, it could then move on to navigating more complex environments, such as mazes with obstacles or varying paths. This step-by-step approach helps the agent to build fundamental skills and confidence, making it better equipped to handle more challenging situations.
Furthermore, curriculum learning can be customized based on the specific needs of the agent or the environment. Developers can create a sequence of tasks tailored to emphasize certain skills or strategies, allowing for a more efficient training process. For example, in a game-playing scenario, an agent might first learn to master basic movements and game mechanics before encountering opponents or more intricate game scenarios. By using curriculum learning, developers can enhance the performance of their RL agents and facilitate a smoother learning process, ultimately leading to better results.