Objective-Behavior Alignment: Diagnostics for MORL Policy Selection

Multi-objective reinforcement learning (MORL) has evolved as a key framework to handle complex decision-making problems involving conflicting objectives. Early methods like Pareto-based algorithms (e.g., NSGA-II, MOEA/D) focused on approximating the Pareto front in the objective space, providing a set of optimal trade-off solutions. However, these approaches primarily evaluate policies based on scalarized objective vectors, neglecting the underlying behavioral differences. Recent studies recognize that policies with similar objective scores can behave very differently, which poses risks in real-world deployments such as autonomous driving, robotics, and finance. Techniques like behavior cloning, trajectory encoding (e.g., VAE, Transformer), and Q-value analysis have been employed to characterize behaviors, but lack systematic tools to quantify and visualize the discrepancy between objective and behavioral spaces. Consequently, there remains a significant gap in understanding how objective proximity correlates with actual policy behavior, especially in high-dimensional, noisy, or sparse reward environments. Addressing this gap is crucial for improving policy robustness, safety, and interpretability in multi-objective RL applications.

The core challenge lies in the disconnect between objective-based policy evaluation and actual behavioral differences. Traditional methods focus on the Pareto front in the objective space, assuming proximity implies similarity. Yet, policies close in objectives can induce vastly different trajectories, risking unsafe or undesirable behaviors in deployment. This problem is exacerbated in complex environments where reward signals are sparse or noisy, making it difficult to rely solely on objective metrics. The key bottleneck is the lack of systematic, automated tools to detect and quantify these behavioral discrepancies, which are often overlooked but critical for safety and robustness. Without such tools, practitioners risk selecting policies that perform well numerically but behave unpredictably in real-world scenarios, undermining trust and safety.

The main innovation is the integration of trajectory-based behavior encoding with geometric analysis to diagnose objective-behavior misalignment. This involves: • Developing a contrastive learning-based encoder that captures behavioral patterns from state-action trajectories. • Introducing metrics like neighborhood preservation, Lipschitz scatterplots, and bias scores to quantify how well the behavior space aligns with the objective space. • Formalizing the concept of objective-behavior misalignment, enabling automatic detection of policy pairs with significant behavioral divergence despite similar objective scores. This approach advances beyond existing methods by providing a scalable, model-agnostic framework that combines quantitative metrics with visual tools, facilitating deeper insights into policy behavior diversity in MORL.

• �� Generate a set of Pareto optimal policies using MORL/D in environments like DST and MuJoCo. • Extract multiple trajectories per policy, capturing state-action sequences. • Train a contrastive trajectory encoder (e.g., Transformer-based) to produce behavioral embeddings, optimizing with InfoNCE loss and regularizers for semantic robustness. • Aggregate embeddings per policy (e.g., mean pooling) to form behavior space representations. • Compute neighborhood preservation, Lipschitz scatterplots, and bias scores to evaluate the relationship between objective and behavior spaces. • Detect policy pairs with high objective proximity but large behavioral differences, flagging potential risks. • Visualize trajectories for qualitative validation, ensuring interpretability of detected biases.

Experiments involved generating policy sets via MORL/D across environments: DST variants and MuJoCo tasks. Trajectories were sampled and encoded, then analyzed using the proposed metrics. In DST, the method identified policy pairs with high bias scores (up to 0.75), indicating behavioral divergence. In MuJoCo environments, neighborhood preservation exceeded 0.8, confirming robustness. Ablation studies compared Transformer encoders with manual features, showing the former's superior detection accuracy. The experiments demonstrated the framework’s scalability, effectiveness in complex environments, and ability to reveal hidden behavioral biases that traditional objective-based evaluation misses.

The framework successfully identified policy pairs with significant behavioral differences despite similar objective scores, with bias scores reaching 0.75 in DST, compared to 0.45 from objective metrics alone. In MuJoCo environments, neighborhood preservation scores exceeded 0.8, confirming the method's robustness. Ablation studies showed Transformer-based encoders improved detection accuracy by over 20%. Visualizations and trajectory inspections validated that the identified policy pairs exhibited distinct behaviors, such as different navigation strategies or movement patterns, which were not apparent from objective values. These results underscore the importance of behavior space analysis for reliable policy selection.

This methodology can be directly applied in safety-critical domains like autonomous vehicles, robotic manipulation, and financial decision-making, where understanding behavioral diversity is vital. It aids engineers in identifying risky policies, refining reward functions, and ensuring robustness before deployment. The approach also supports policy interpretability, enabling stakeholders to visualize and comprehend behavioral variations. In the long term, integrating this framework into automated policy tuning and multi-agent systems could significantly enhance RL safety, transparency, and trustworthiness, fostering broader adoption in industry.

The approach relies on high-quality trajectory sampling and encoder training, which may be computationally intensive and sensitive to noise. Its effectiveness diminishes in environments with sparse rewards or highly stochastic dynamics. Automated relevance assessment of detected biases is limited; human judgment remains essential. In high-dimensional or complex scenarios, the current metrics and models may struggle to accurately capture behavioral nuances, necessitating further methodological improvements. Future work should focus on robustness enhancements, scalability, and automated bias relevance evaluation.