  One of the main challenges in real-world reinforcement learning is to learn
successfully from limited training samples. We show that in certain settings,
the available data can be dramatically increased through a form of multi-task
learning, by exploiting an invariance property in the tasks. We provide a
theoretical performance bound for the gain in sample efficiency under this
setting. This motivates a new approach to multi-task learning, which involves
the design of an appropriate neural network architecture and a prioritized
task-sampling strategy. We demonstrate empirically the effectiveness of the
proposed approach on two real-world sequential resource allocation tasks where
this invariance property occurs: financial portfolio optimization and meta
federated learning.
