Many real-world decision problems involve conflicting objectives. Multi-objective reinforcement learning (MORL) extends standard RL to optimize multiple objectives simultaneously, producing policy sets that capture different trade-offs. However, MORL research often relies on simplified benchmarks with limited real-world relevance. We present MORL4Water, a modular toolkit for creating realistic MORL environments in water resource management. Built on MO-Gymnasium, MORL4Water enables scenario construction from real data and systematic evaluation of MORL methods. We illustrate its use on the Nile and Susquehanna rivers, benchmarking several MORL algorithms against EMODPS, a domain-specific baseline. Beyond standard performance metrics, we analyze solution sets to reveal differences in exploration, scalability, and trade-off diversity. Our results show that most state-of-the-art MORL algorithms underperform relative to EMODPS, especially in higher-dimensional settings, and highlight the value of solution-set analysis for robust, real-world applications. ... Read more

Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce JUSTICE, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, JUSTICE generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy. ... Read more

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields. We provide an overview of the advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and ethics. We synthesize these methods drawing from different fields of research to build a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions. ... Read more