A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

• URL: http://arxiv.org/abs/2501.10529v1
• Date: Fri, 17 Jan 2025 20:07:11 GMT
• Title: A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
• Authors: Sergio Rozada, Santiago Paternain, Juan Andres Bazerque, Antonio G. Marques,
• Abstract summary: In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches. A low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments.
• Score: 10.359616364592073
• License:
• Abstract: In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices.

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