Repeated Deceptive Path Planning against Learnable Observer

2026-05-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

Researchers introduce Repeated Deceptive Path Planning (RDPP), a new formulation for agents to conceal their true destination from learnable, adaptive observers over multiple interactions. Unlike existing Deceptive Path Planning (DPP) methods that assume static observers, RDPP models adversaries who learn from historical trajectories, a scenario where traditional DPP fails due to accumulating adaptation lag. To address this, the Deceptive Meta Planning (DeMP) framework is proposed, featuring a two-level optimization structure. DeMP combines episode-level adaptation for short-term policy adjustments with meta-level updates that leverage cross-episode feedback to anticipate observer learning dynamics and accelerate future adaptation. Experiments in grid-world and pirate deception environments demonstrate that DeMP significantly outperforms existing approaches by maintaining sustained deception and competitive path costs against evolving observers, mitigating the adaptation lag inherent in reactive strategies.

Key takeaway

For research scientists developing multi-agent systems in adversarial environments, you should consider implementing meta-learning frameworks like DeMP to achieve sustained deception. Traditional reactive planning methods are insufficient against learning observers, leading to rapid performance degradation. By proactively anticipating adversary learning dynamics through meta-level updates, you can maintain deceptive effectiveness over extended interactions, crucial for applications like critical goods transportation or military operations.

Key insights

Sustained deception against learning adversaries requires proactive, multi-level adaptation to mitigate accumulating lag.

Principles

• Reactive adaptation causes cumulative lag.
• Anticipate observer learning dynamics.
• Balance historical data with timely updates.

Method

DeMP uses a two-level optimization: episode-level policy adjustment after each interaction and meta-level updates every M episodes to refine policy initialization based on cross-episode feedback.

In practice

• Implement meta-learning for adversarial planning.
• Use Soft Actor-Critic (SAC) for policy optimization.
• Vary meta-episode parameter M for optimal balance.

Topics

• Repeated Deceptive Path Planning
• Deceptive Meta Planning
• Learnable Observers
• Goal Recognition
• Meta-level Optimization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.