What is curriculum learning in reinforcement learning?

Curriculum Learning in Reinforcement Learning Curriculum learning in reinforcement learning (RL) is a training strategy where an agent learns tasks in a structured order, starting with simpler scenarios and gradually progressing to more complex ones. The goal is to mimic how humans learn—by building foundational skills before tackling harder challenges. Instead of exposing the agent to random or uniformly difficult environments from the start, the training process is guided by a predefined or adaptive “curriculum” that controls task difficulty. This approach helps the agent avoid getting stuck in local optima or failing outright due to overwhelming complexity early in training.

Examples and Implementation A practical example is training a robot to navigate. Initially, the agent might learn to move in an empty room, then add static obstacles, and finally introduce dynamic obstacles like moving objects. Another example is game AI: an agent could first master basic levels with limited enemies before advancing to levels with faster opponents or complex objectives. The curriculum can be manually designed (e.g., handcrafted difficulty tiers) or automated. For instance, in reverse curriculum learning, training starts near a goal state (e.g., a robot arm close to a target object) and expands the starting positions as the agent improves. Tools like OpenAI Gym environments or custom wrappers can adjust parameters (e.g., obstacle density, physics properties) to scale difficulty.

Benefits and Challenges The primary benefit of curriculum learning is improved training efficiency. By breaking down complex tasks, the agent learns reusable skills and avoids wasting time on scenarios far beyond its current capability. This often leads to faster convergence and better final performance compared to unstructured training. However, designing an effective curriculum requires careful balancing. If the progression is too slow, training becomes inefficient; if too fast, the agent may fail to generalize. Automated methods, like measuring the agent’s success rate to trigger difficulty increases, can help, but they add complexity. For developers, experimenting with curriculum design—such as adjusting task sequences or reward thresholds—is often necessary to tailor the approach to specific problems.