mlsanityData to 14 Oct 2026

MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation

arXiv 2603.05760 ↗First posted 5 Mar 2026cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and competing objectives. However, its deployment in dynamic environments is hindered by the need for task-specific retraining and substantial computational cost. We introduce MIRACL (Meta multI-objective Reinforcement leArning with Composite Learning), a hierarchical Meta-MORL framework that allows for a few-shot generalisation across diverse tasks. MIRACL decomposes each task into structured subproblems for efficient policy adaptation and meta-learns a global policy across tasks using a Pareto-based adaptation strategy to encourage diversity in meta-training and fine-tuning. To our knowledge, this is the first integration of Meta-MORL with such mechanisms in combinatorial optimisation. Although validated in the supply chain domain, MIRACL is theoretically domain-agnostic and applicable to broader dynamic multi-objective decision-making problems. Empirical evaluations show that MIRACL outperforms conventional MORL baselines in simple to moderate tasks, achieving up to 10% higher hypervolume and 5% better expected utility. These results underscore the potential of MIRACL for robust, efficient adaptation in multi-objective problems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 25 references that are in the map. First posted on arXiv 5 Mar 2026.

Builds on

10 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation26 Jul
  2. 2024A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments16 Jul

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  4. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 503
  5. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  6. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 177
  7. 2018Meta-Learning for Multi-objective Reinforcement Learningcited by 34
  8. 2023Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Frameworkcited by 31

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization
  2. 2025Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization
  3. 2025Pareto Set Learning for Multi-Objective Reinforcement Learning
  4. 2025Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation
  5. 2026Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers
  6. 2025Limitations of Scalarisation in MORL: A Comparative Study in Discrete Environments
  7. 2024Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning
  8. 2024C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front