Goal-Conditioned Reinforcement Learning: Problems and Solutions. (arXiv:2201.08299v1 [cs.AI])

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 …

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