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arXiv · 2102.09361

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

Desmond Cai·Shiau Hong Lim·Laura Wynter
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Abstract

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.

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Desmond Cai, Shiau Hong Lim, Laura Wynter. 2021-02-18. Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning. https://arxiv.org/abs/2102.09361

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