"Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning"

This paper makes complex Multi-objective reinforcement learning (MORL) policies understandable by clustering them based on both behavior and objectives

When AI gives you too many options, this clustering trick saves the day

https://arxiv.org/abs/2411.04784v1

🎯 Original Problem:

Multi-objective reinforcement learning (MORL) generates multiple policies with different trade-offs, but these solution sets are too large and complex for humans to analyze effectively. Decision makers struggle to understand relationships between policy behaviors and their objective outcomes.

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🛠️ Solution in this Paper:

→ Introduces a novel clustering approach that considers both objective space (expected returns) and behavior space (policy actions)

→ Uses Highlights algorithm to capture 5 key states that represent each policy's behavior

→ Applies PAN (Pareto-Set Analysis) clustering to find well-defined clusters in both spaces simultaneously

→ Employs bi-objective evolutionary algorithm to optimize clustering quality across both spaces

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💡 Key Insights:

→ First research to tackle MORL solution set explainability

→ Different policies with similar trade-offs can exhibit vastly different behaviors

→ Combining objective and behavior analysis reveals deeper policy insights

→ Makes MORL more practical for real-world applications

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📊 Results:

→ Outperformed traditional k-medoids clustering in MO-Highway and MO-Lunar-lander environments

→ Showed comparable performance in MO-Reacher and MO-Minecart scenarios

→ Successfully demonstrated practical application through highway environment case study