How does curriculum learning help in RL?

Curriculum learning improves reinforcement learning (RL) by structuring tasks in a progression from simple to complex, similar to how humans learn step-by-step. Instead of exposing the agent to the full complexity of a problem immediately, curriculum learning breaks the task into manageable stages. This approach helps the agent build foundational skills and gradually adapt to harder challenges. For example, training a robot to walk might start with balancing on flat ground before introducing slopes or obstacles. By controlling the difficulty curve, the agent avoids getting stuck in local optima caused by overwhelming initial complexity.

One key benefit is improved sample efficiency. In traditional RL, agents often waste effort exploring irrelevant actions in complex environments. A curriculum reduces this by focusing early training on simpler scenarios where rewards are easier to attain. For instance, in a maze navigation task, the agent might first learn to solve small, sparse mazes before tackling larger ones with more dead ends. This allows the agent to master basic navigation strategies (e.g., wall-following) that generalize to harder mazes. Experiments in games like Montezuma’s Revenge—a notoriously difficult RL benchmark due to sparse rewards—show that curriculum-based agents achieve higher scores faster by first practicing sub-tasks like collecting keys or avoiding enemies.

Curriculum learning also addresses exploration challenges. In complex environments, agents might never discover critical states or rewards without guidance. A curriculum acts as a scaffold, directing exploration toward meaningful milestones. For example, in a robot manipulation task, early training could involve placing objects closer to the gripper, ensuring the agent learns grasping before moving to precise placement. This structured exploration is especially useful in domains with delayed rewards, as it provides intermediate goals that keep the agent motivated. Without a curriculum, the same agent might fail to ever grasp an object, stalling progress entirely. By incrementally raising the difficulty, the agent’s policy evolves in a stable, targeted way, reducing the risk of catastrophic forgetting or unstable training dynamics.