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One of the main challenges in real-world reinforcement learning is to learnsuccessfully from limited training samples. We show that in certain settings,the available data can be dramatically increased through a form of multi-tasklearning, by exploiting an invariance property in the tasks. We provide atheoretical performance bound for the gain in sample efficiency under thissetting. This motivates a new approach to multi-task learning, which involvesthe design of an appropriate neural network architecture and a prioritizedtask-sampling strategy. We demonstrate empirically the effectiveness of theproposed approach on two real-world sequential resource allocation tasks wherethis invariance property occurs: financial portfolio optimization and metafederated learning.

Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning