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.

通过使用函数编码器来表示奖励函数或转换函数，学习代理机器人在运行时如何与先前的任务相关联的方法，并通过在强化学习算法中引入功能编码器任务表示来展示最先进的数据效率、渐近性能和训练稳定性。