  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.
