Multi-Task Optimization over Networks of Tasks

• URL: http://arxiv.org/abs/2604.21991v1
• Date: Thu, 23 Apr 2026 18:18:20 GMT
• Title: Multi-Task Optimization over Networks of Tasks
• Abstract summary: We introduce MONET, a multi-task optimization algorithm that models the task space as a graph.<n>Social learning generates candidates from neighboring nodes via crossover, with individual learning, which refines a node's own solution independently via mutation.<n>We show that MONET matches or exceeds the performance of existing MAP-Elites-based baselines across all four domains.
• Score: 1.7499351967216341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that do scale beyond a thousand tasks are mostly MAP-Elites variants and rely on a fixed, discretized archive that disregards the topology of the task space. We introduce MONET (Multi-Task Optimization over Networks of Tasks), a multi-task optimization algorithm that models the task space as a graph: tasks are nodes, and edges connect tasks in the task parameter space. This representation enables knowledge transfer between tasks and remains tractable for high-dimensional problems while exploiting the topology of the task space. MONET combines social learning, which generates candidates from neighboring nodes via crossover, with individual learning, which refines a node's own solution independently via mutation. We evaluate MONET on four domains (archery, arm, and cartpole with 5,000 tasks each; hexapod with 2,000 tasks) and show that it matches or exceeds the performance of existing MAP-Elites-based baselines across all four domains.

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