Automatic Curriculum Learning
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Automatic Curriculum Learning (ACL) is an approach used in machine learning, and more specifically, in reinforcement learning. The main idea of ACL is to gradually expose the learning agent to more complex problems or tasks as its competence improves, in the same way that human education uses a curriculum to structure learning.[1] This approach can make learning more efficient and effective, especially when dealing with complex tasks that can be decomposed into simpler sub-tasks.
ACL can also be said to constitute methods for automatically generating an appropriate curriculum for a learning agent. This could be based on the agent's past performance, or it could involve a separate learning process that is used to generate the curriculum.
For instance, one simple approach is to have the agent always choose to practice the task that it is currently worst at, but there are many more sophisticated methods. In some cases, the learning agent and the curriculum-generating process co-evolve together, with the curriculum getting progressively harder as the agent gets better, and the agent's capabilities informing the curriculum generation.
The difficulty in applying this idea in a machine learning context is in determining what constitutes an appropriate curriculum. An overly difficult curriculum could lead to an agent getting "stuck" and not learning anything, while an overly easy curriculum could slow down learning or limit the eventual capabilities of the agent.
While this field is still developing, ACL holds promise as a way of addressing some of the challenges in training artificial intelligence, particularly for tasks that require a long sequence of correct actions to be learned, or where the space of potential actions is very large.
References[edit]
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- ↑ Portelas, Rémy; Colas, Cédric; Weng, Lilian; Hofmann, Katja; Oudeyer, Pierre-Yves (2020). "Automatic Curriculum Learning for Deep RL: A Short Survey". arXiv:2003.04664 [cs.LG].
