Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach
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| Format: | Preprint |
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2024
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| author | Vora, Manav Shomorony, Ilan Ornik, Melkior |
| author_facet | Vora, Manav Shomorony, Ilan Ornik, Melkior |
| contents | We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a planner must decide (i) when to apply a restorative action and (ii) which subset of agents to treat in parallel. The global budget limits the total number of restorations, while the capacity constraint bounds the number of simultaneous actions, turning naïve dynamic programming into a combinatorial search that scales exponentially with the number of agents. We propose a two-stage solution that remains tractable for large systems. First, a Linear Sum Assignment Problem (LSAP)-based grouping partitions the agents into r disjoint sets (r = capacity) that maximise diversity in expected time-to-failure, allocating budget to each set proportionally. Second, a meta-trained PPO policy solves each sub-MDP, leveraging transfer across groups to converge rapidly. To validate our approach, we apply it to the problem of scheduling repairs for a large team of industrial robots, constrained by a limited number of repair technicians and a total repair budget. Our results demonstrate that the proposed method outperforms baseline approaches in terms of maximizing the average uptime of the robot team, particularly for large team sizes. Lastly, we confirm the scalability of our approach through a computational complexity analysis across varying numbers of robots and repair technicians. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_21249 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach Vora, Manav Shomorony, Ilan Ornik, Melkior Machine Learning Artificial Intelligence We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a planner must decide (i) when to apply a restorative action and (ii) which subset of agents to treat in parallel. The global budget limits the total number of restorations, while the capacity constraint bounds the number of simultaneous actions, turning naïve dynamic programming into a combinatorial search that scales exponentially with the number of agents. We propose a two-stage solution that remains tractable for large systems. First, a Linear Sum Assignment Problem (LSAP)-based grouping partitions the agents into r disjoint sets (r = capacity) that maximise diversity in expected time-to-failure, allocating budget to each set proportionally. Second, a meta-trained PPO policy solves each sub-MDP, leveraging transfer across groups to converge rapidly. To validate our approach, we apply it to the problem of scheduling repairs for a large team of industrial robots, constrained by a limited number of repair technicians and a total repair budget. Our results demonstrate that the proposed method outperforms baseline approaches in terms of maximizing the average uptime of the robot team, particularly for large team sizes. Lastly, we confirm the scalability of our approach through a computational complexity analysis across varying numbers of robots and repair technicians. |
| title | Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.21249 |