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Multi-agent Reinforcement Learning Framework

August 20, 2026

Every season, a farming household in the Harare foodshed makes a decision. Plant again and hope the rains hold. Take on other work. Send one member to the city for part of the year. Or leave altogether.

None of these choices is made in isolation. They depend on last year’s harvest, on what neighbours are doing, on whether there is a safety net if the crop fails, and on who in the household can travel. Multiply that by thousands of households, repeat it over thirty years of a changing climate, and you have one of the hardest questions in climate adaptation: where will people stay or move, and what would have kept them food-secure?

Policymakers have to answer in advance: a subsidy programme or a rural safety net must be designed and funded before anyone knows whether it works. Within the SAFE4ALL project, Neuralio AI has developed the Multi-Agent Reinforcement Learning Framework to supply something close to the missing test — a virtual foodshed in which policies can be tried before they reach the real world.

A Virtual Copy of a Real Foodshed

The framework builds a working model of an actual place — Narok County in Kenya, the area around Tamale in Ghana, Marondera District within the Harare foodshed in Zimbabwe — and populates it with thousands of individual agents.

These agents are organised in four levels, mirroring how decisions are really made. Households decide what to grow and whether to stay. Community cooperatives pool resources and share information. Regional foodshed managers allocate support. National policymakers set the rules everyone else operates under. Influence runs downward, upward and sideways between them.

The simulation is driven by real data — observed and projected climate, satellite Earth observation, agricultural statistics, household surveys and migration records — and the climate futures it explores follow the standard IPCC scenarios.

Agents That Learn, Rather Than Follow Rules

What distinguishes the framework from a conventional simulation is that its agents are not given a fixed script.

Each household uses reinforcement learning. Faced each season with a choice between farming on, diversifying its income, sending someone away seasonally, or migrating permanently, it learns from the outcome, weighing income and food security against the cost of moving and the value of the social network it would leave behind.

This matters because adaptation is adaptive. Real households do not respond to a second drought as they did to the first. They learn, hedge and change strategy, and what emerges in aggregate is often not what a rule-based model would produce — including the ways a well-intentioned policy can fail.

Simulated household decisions across Narok County, Kenya. Each point is a household; the outline shows whether it is male- or female-headed and the fill shows what it chose to do — stay and farm, diversify, migrate seasonally, or migrate permanently.
Asking Questions in Plain Language

A model this complex is of little use if only its authors can operate it. It is therefore delivered as a platform with a conversational interface: the user draws an area of interest on a map and types a question in ordinary language — simulate migration under a moderate climate scenario over twenty years, say. The platform runs it and returns interactive maps, timelines and a written report in plain language. No coding is required, and no familiarity with agent-based modelling.

That opens up the questions worth asking. When is a foodshed at risk of falling below the level of self-sufficiency at which it depends on outside supply, and what would prevent it? And how does migration differ by gender — male- and female-headed households in the same region face substantially different options, a difference aggregate figures conceal entirely.

The platform in use. The user has drawn an area of interest around Tamale in northern Ghana and asked, in plain language, for a thirty-year migration simulation under a high-emissions scenario. Maps, timelines and a written report are returned.
The same question asked of all three SAFE4ALL case study regions at once: Tamale in Ghana, Narok County in Kenya and Marondera in Zimbabwe, compared under an identical climate scenario.
Scenarios, Not Predictions

It is worth being clear about what this does not offer. The framework cannot predict how many people will migrate from a particular district. What it does is explore how climate stress, household circumstances and policy interact, and which levers change the outcome in which direction. Its value lies in comparison: this policy against that one, this region against another, an intervention against its absence. Each scenario is run many times over and reported as a range with confidence intervals rather than a single answer.

The platform is working across all three case study regions, and attention has now turned to calibrating it against observed migration and household data, together with partners in those countries.

The Multi-Agent Reinforcement Learning Framework is one of the tools developed within the SAFE4ALL project, each addressing a different aspect of climate services and disaster risk management.

Neuralio AI

Neuralio AI is an advanced technology company specialising in Earth Observation, artificial intelligence, and Earth System Modeling. Its multidisciplinary team develops data-driven solutions that transform complex environmental information into actionable intelligence for decision-makers.

Within SAFE4ALL, Neuralio AI contributes advanced AI-based tools supporting climate resilience and sustainable development. Its Hyperlocal Weather Intelligence Engine provides precise, bias-corrected meteorological forecasts, while its Multi-Agent Reinforcement Learning Framework combines Earth Observation and other data sources to simulate socio-environmental dynamics. These technologies support strategic decision-making in areas such as agriculture, disaster response, land use optimisation, and climate adaptation.