RL-Driven Sustainable Land-Use Allocation for the Lake Malawi Basin
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Unsustainable land-use practices in ecologically sensitive regions threaten biodiversity, water resources, and the livelihoods of millions. This paper presents a deep reinforcement learning (RL) framework for optimizing land-use allocation in the Lake Malawi Basin to maximize total ecosystem service value (ESV). Drawing on the benefit transfer methodology of Costanza et al., we assign biome-specific ESV coefficients -- locally anchored to a Malawi wetland valuation -- to nine land-cover classes derived from Sentinel-2 imagery. The RL environment models a 50x50 cell grid at 500m resolution, where a Proximal Policy Optimization (PPO) agent with action masking iteratively transfers land-use pixels between modifiable classes. The reward function combines per-cell ecological value with spatial coherence objectives: contiguity bonuses for ecologically connected land-use patches (forest, cropland, built area etc.) and buffer zone penalties for high-impact development adjacent to water bodies. We evaluate the framework across three scenarios: (i) pure ESV maximization, (ii) ESV with spatial reward shaping, and (iii) a regenerative agriculture policy scenario. Results demonstrate that the agent effectively learns to increase total ESV; that spatial reward shaping successfully steers allocations toward ecologically sound patterns, including homogeneous land-use clustering and slight forest consolidation near water bodies; and that the framework responds meaningfully to policy parameter changes, establishing its utility as a scenario-analysis tool for environmental planning.
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 2 of its 24 references that are in the map. First posted on arXiv 4 Apr 2026.
Builds on
2 of 24 references.
References · 2
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management
- 2024Reinforcement Learning for Sociohydrology
- 2024Learning-based agricultural management in partially observable environments subject to climate variability
- 2025Multi-Objective Reinforcement Learning for Water Management
- 2026Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection
- 2026PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning
- 2024A Comparative Study of Deep Reinforcement Learning for Crop Production Management
- 2024Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm