Can MARL synthesize optimal climate policies?
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
April 18, 2025
https://arxiv.org/pdf/2504.12777This paper proposes using Multi-Agent Reinforcement Learning (MARL) to improve climate policy development. It suggests integrating MARL with Integrated Assessment Models (IAMs) to simulate interactions between different regions or stakeholders (agents) as they make decisions about climate policies (actions) within a simulated global environment. This framework allows exploration of a wider range of policy options than traditional methods.
Key points for LLM-based multi-agent systems:
- IAMs as Environments: IAMs can be used as complex simulation environments for MARL agents.
- Heterogeneous Agents: The framework emphasizes the importance of modeling diverse agents with different goals and behaviors, which aligns well with LLM-based agents' ability to exhibit diverse personalities and strategies.
- Reward Definition: Defining appropriate reward functions is crucial and can incorporate multiple objectives like sustainability and equity. LLMs could play a role in generating or refining these reward functions.
- Scalability and Uncertainty: The paper highlights the challenges of scaling MARL to many agents and handling the inherent uncertainties of climate models. Efficient training and uncertainty quantification are important areas for future research, and LLMs might offer new approaches here.
- Explainability: Interpreting the decisions made by MARL agents within the complex IAM environment is challenging. LLMs could assist in generating human-understandable explanations for agent policies and predicted outcomes.
- Solution Validation: Evaluating the realism and robustness of MARL-generated policies is critical. LLMs could help create more realistic agent behaviors or simulate various initial conditions to stress-test policies.
- Distribution of Solutions: The paper suggests finding multiple optimal solutions rather than a single one to increase resilience against unexpected events. LLMs could contribute to methods for discovering diverse optimal or near-optimal policy sets.