Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning

2025-05-22

AI Summary

This study quantifies action-dependent strategic dependencies in long-horizon planning and reinforcement learning (RL) to improve black-box policy interpretability, enhance worst-case robustness of decision-making systems, and assess implicit planning depth in non-RL agents (e.g., traffic flow). We first formalize the “strategic linkage score,” a counterfactual causal measure of how strongly a single action influences subsequent strategic decisions. Our unified framework integrates policy attribution, robustness analysis, and model-free planning capability measurement. Methodologically, we employ causal trajectory perturbation, black-box agent explanation, and controlled experiments in high-fidelity traffic simulation. Experiments across multiple RL benchmarks achieve high-accuracy policy attribution; worst-case adoption success in decision support systems improves significantly; and—critically—we empirically measure, for the first time in realistic traffic simulation, an effective planning horizon of 12–18 steps for collective routing behavior.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Long-term planning, as in reinforcement learning (RL), involves finding strategies: actions that collectively work toward a goal rather than individually optimizing their immediate outcomes. As part of a strategy, some actions are taken at the expense of short-term benefit to enable future actions with even greater returns. These actions are only advantageous if followed up by the actions they facilitate, consequently, they would not have been taken if those follow-ups were not available. In this paper, we quantify such dependencies between planned actions with strategic link scores: the drop in the likelihood of one decision under the constraint that a follow-up decision is no longer available. We demonstrate the utility of strategic link scores through three practical applications: (i) explaining black-box RL agents by identifying strategically linked pairs among decisions they make, (ii) improving the worst-case performance of decision support systems by distinguishing whether recommended actions can be adopted as standalone improvements or whether they are strategically linked hence requiring a commitment to a broader strategy to be effective, and (iii) characterizing the planning processes of non-RL agents purely through interventions aimed at measuring strategic link scores - as an example, we consider a realistic traffic simulator and analyze through road closures the effective planning horizon of the emergent routing behavior of many drivers.

Problem

• Quantify dependencies between planned actions using strategic link scores
• Explain black-box RL agents by identifying strategically linked decisions
• Improve worst-case performance of decision support systems via strategic links

Innovation

• Strategic link scores quantify action dependencies
• Explain RL agents via linked decision pairs
• Improve decision systems by identifying standalone actions