Reinforcement Learning for Dynamic Decision Making in Ecology: Optimizing Conservation and Resource Management

Abstract

Reinforcement Learning (RL) provides a powerful framework for adaptive decision-making in ecological management, particularly in addressing challenges like conservation and natural resource use. This paper investigates the application of RL to key ecological problems, focusing on the optimization of interventions such as setting fishing quotas and establishing protected areas. Three RL algorithms—Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods—were applied to a simulated environment that models complex ecological dynamics over time. The performance of these algorithms was evaluated based on three metrics: average reward, population sustainability, and resource utilization. Results demonstrate that Policy Gradient methods achieved the highest balance between long-term ecological sustainability and resource efficiency, followed by DQN, which performed moderately well in resource utilization and sustainability. Q-Learning, is effective in maximizing short-term resource use, showed limitations in maintaining population stability. These findings suggest that RL algorithms, particularly Policy Gradient methods, offer significant potential for optimizing decision-making in conservation and resource management under uncertain environmental conditions.