Zero-Shot Reinforcement Learning via Function Encoders

Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The difficulty lies in finding a good representation for the current task so that the agent understands how it relates to previously seen tasks. To achieve zero-shot transfer, we introduce the function encoder, a representation learning algorithm which represents a function as a weighted combination of learned, non-linear basis functions. By using a function encoder to represent the reward function or the transition function, the agent has information on how the current task relates to previously seen tasks via a coherent vector representation. Thus, the agent is able to achieve transfer between related tasks at run time with no additional training. We demonstrate state-of-the-art data efficiency, asymptotic performance, and training stability in three RL fields by augmenting basic RL algorithms with a function encoder task representation.

@article{ingebrand2025_2401.17173,
  title={ Zero-Shot Reinforcement Learning via Function Encoders },
  author={ Tyler Ingebrand and Amy Zhang and Ufuk Topcu },
  journal={arXiv preprint arXiv:2401.17173},
  year={ 2025 }
}