# Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation

arXiv: https://arxiv.org/abs/2606.22146 · HTML: https://mlsanity.com/paper/2606.22146/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Low Data Drug Discovery with One-shot Learning](https://mlsanity.com/paper/1611.03199/) (2016)
- [AI Benchmark: All About Deep Learning on Smartphones in 2019](https://mlsanity.com/paper/1910.06663/) (2019)
- [Meta-Learning for Multi-objective Reinforcement Learning](https://mlsanity.com/paper/1811.03376/) (2018)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer](https://mlsanity.com/paper/2305.03463/) (2023)
- [Evolutionary Reinforcement Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2402.13296/) (2024)
- [Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach](https://mlsanity.com/paper/2402.12260/) (2024)
- [A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments](https://mlsanity.com/paper/2407.11489/) (2024)
- [Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation](https://mlsanity.com/paper/2507.19788/) (2025)
- [MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation](https://mlsanity.com/paper/2603.05760/) (2026)

## Abstract (arXiv)

Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environments and preference settings. However, conventional few-shot methods usually fine-tune from a single shared meta-policy, which can reduce solution diversity and limit exploration of the Pareto front, especially in high-dimensional combinatorial problems such as supply chain optimisation. We propose a population-based Meta-reinforcement learning framework that combines decomposition with evolutionary search in scalarisation weight space. The framework maintains a population of weight vectors, each associated with a distinct meta-policy trained through gradient-based meta-learning, and iteratively refines this population through elitist selection, crossover, and mutation guided by hypervolume and entropy contributions. We evaluate the method in a multi-objective supply chain setting with conflicting economic, environmental, and social goals, and further test its generality on standard reinforcement learning problems. The results show that the proposed approach yields more diverse, better distributed Pareto front approximations, improves cross-task adaptation, increases hypervolume by up to 32% over Meta-multi-objective reinforcement learning in the complex case, and attains the lowest average Hausdorff distance among all compared methods.