{"slug": "adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity", "title": "Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity", "summary": "A new arXiv paper (2610.07475v1) presents a finite-depth framework for estimating policy sensitivity in reinforcement learning, approximating the policy Hessian and mixed derivative from a reference environment to predict how a locally optimal policy shifts when another agent's behavior changes. The authors derive truncation-error bounds for the approximated derivatives and policy sensitivity that are nonincreasing with propagation depth and vanish at full-horizon propagation. In a belief-driven pursuit-evasion game, the method generally reduced derivative-estimation errors as propagation depth increased, outperformed baseline methods in estimation accuracy and policy adaptation, and its sensitivity-based initialization improved zero-shot return over direct transfer while aiding subsequent fine-tuning in the target environment.", "body_md": "arXiv:2610.07475v1 Announce Type: new \nAbstract: Adapting a reinforcement learning policy to changes in another agent's behavior typically requires a large amount of new interaction data. Policy sensitivity provides a first-order prediction of how a locally optimal policy changes with a behavioral parameter, but its computation requires second-order derivatives whose effects propagate across future interactions. We develop a finite-depth framework to estimate this sensitivity by approximating the policy Hessian and mixed derivative using information from a reference environment. The method features an adjustable propagation depth which determines where derivative propagation along the trajectory is truncated. We characterize the derivative contributions omitted by finite-depth propagation and derive truncation-error bounds for the approximated derivatives and resulting policy sensitivity. The bounds are nonincreasing with propagation depth and vanish at full-horizon propagation. Using a belief-driven pursuit-evasion game as a validation scenario, the proposed method generally achieves lower derivative-estimation errors as the propagation depth increases and outperforms the baseline methods in both estimation accuracy and policy adaptation. The sensitivity-based initialization improves zero-shot return over direct transfer, and also shows advantages for the subsequent fine-tuning in the target environment.", "url": "https://wpnews.pro/news/adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity", "canonical_source": "https://www.machinebrief.com/news/adapting-to-changes-in-agent-behavior-via-finite-depth-polic-yuo0", "published_at": "2026-10-07 04:00:00+00:00", "updated_at": "2026-10-07 04:48:10.049561+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "artificial-intelligence"], "entities": ["arXiv", "2610.07475v1"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity", "markdown": "https://wpnews.pro/news/adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity.md", "text": "https://wpnews.pro/news/adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity.txt", "jsonld": "https://wpnews.pro/news/adapting-to-changes-in-agent-behavior-via-finite-depth-policy-sensitivity.jsonld"}}