Curriculum learning in reinforcement learning (RL) is a training strategy where the learning process is structured in a way that gradually increases the difficulty of the tasks presented to the learning agent. Instead of starting with a very challenging problem that might overwhelm the agent, curriculum learning allows the agent to first tackle easier tasks to build foundational skills and understanding. This progressive approach helps the agent learn more effectively, as it can develop strategies that are then refined as it encounters more complex scenarios.
For example, consider an RL agent designed to play a video game. If the agent is introduced to the game environment with simple tasks, such as collecting a single item, it can focus on understanding the basic movement mechanics and item interaction. Once it masters those initial tasks, the curriculum can advance to more complicated challenges, such as navigating through obstacles or completing time-limited objectives. This step-by-step learning allows the agent to accumulate knowledge and experience, making it easier for it to handle the complexities of the game in subsequent training stages.
Additionally, curriculum learning can enhance the convergence speed of the learning process. By simplifying initial tasks, the agent can achieve success more frequently in earlier stages, which provides more immediate rewards. These rewards can reinforce learning and improve motivation, allowing for faster overall improvement. Furthermore, when the agent eventually faces harder tasks, its prior experiences with simpler tasks may provide a better starting point for learning, reducing the amount of trial and error necessary. Therefore, integrating curriculum learning into RL frameworks can significantly boost the efficiency and effectiveness of the training process, leading to better-performing agents.