Reinforcement learning is a powerful paradigm that has gained popularity across multiple domains. However, applying reinforcement learning may come at the cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of non-productivity. Curriculum learning provides a suitable alternative by arranging a sequence of tasks of increasing complexity, with the aim of reducing the overall cost of learning. Here we demonstrate the application of curriculum learning for drug discovery. We implement curriculum learning in the de novo design platform REINVENT, and apply it to illustrative molecular design problems of different complexities. The results show both accelerated learning and a positive impact on the quality of the output when compared with standard policy-based reinforcement learning.

强化学习是一种强大的范式，已在多个领域广受欢迎。然而，应用强化学习可能会以代理与环境之间的多次交互为代价。当来自环境的单一反馈缓慢或计算量大时，这种成本尤其明显，从而导致长时间的非生产力。课程学习通过安排一系列复杂性越来越高的任务，提供了一种合适的替代方案，目的是降低学习的总体成本。在这里，我们展示了课程学习在药物发现中的应用。我们在从头设计平台 REINVENT 中实施课程学习，并将其应用于不同复杂性的说明性分子设计问题。