论文标题
目标条件的强化学习：问题和解决方案
Goal-Conditioned Reinforcement Learning: Problems and Solutions
论文作者
论文摘要
与一组复杂的RL问题有关的目标条件加固学习（GCRL）训练代理在特定情况下实现不同的目标。与仅根据州或观察结果了解政策的标准RL解决方案相比，GCRL还要求代理商根据不同的目标做出决策。在这项调查中，我们全面概述了GCRL的挑战和算法。首先，我们回答该领域研究的基本问题。然后，我们解释了如何代表目标，并介绍了如何从不同角度设计现有解决方案。最后，我们得出结论，并讨论最近研究重点的潜在未来前景。
Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the standard RL solutions that learn a policy solely depending on the states or observations, GCRL additionally requires the agent to make decisions according to different goals. In this survey, we provide a comprehensive overview of the challenges and algorithms for GCRL. Firstly, we answer what the basic problems are studied in this field. Then, we explain how goals are represented and present how existing solutions are designed from different points of view. Finally, we make the conclusion and discuss potential future prospects that recent researches focus on.
