Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization

Abstract

Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to align the RL policy behavior with their preferences through intuitive pairwise comparisons of candidate portfolios based on their Sharpe ratios and aggregate ESG scores. We systematically evaluate our framework by employing prompt-conditioned LLMs as synthetic preference oracles under varied regional conditions and assess how the methodology adapts to heterogeneous investment behaviors and sustainability preferences. The experiments demonstrate feasibility in a controlled synthetic setting and characterize sensitivity to oracle and prompt design.