# Repeated Deceptive Path Planning against Learnable Observer

arXiv: https://arxiv.org/abs/2605.07174 · HTML: https://mlsanity.com/paper/2605.07174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 11 of 11)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals](https://mlsanity.com/paper/1906.10571/) (2019)
- [Deceptive Reinforcement Learning for Privacy-Preserving Planning](https://mlsanity.com/paper/2102.03022/) (2021)
- [Deception in Optimal Control](https://mlsanity.com/paper/1805.03090/) (2018)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [Deceptive Reinforcement Learning in Model-Free Domains](https://mlsanity.com/paper/2303.10838/) (2023)
- [A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion](https://mlsanity.com/paper/1907.00459/) (2019)
- [The Eater and the Mover Game](https://mlsanity.com/paper/2306.03877/) (2023)

## Abstract (arXiv)

We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work assumes static, non-learning observers, real-world adversaries-such as in critical goods transportation or military operations-can adapt by learning from historical trajectories. To address this gap, we introduce Repeated Deceptive Path Planning (RDPP), a new formulation that explicitly models learnable observers. We show that existing DPP methods fail under this setting, as they cannot adapt to evolving adversarial predictions. While incorporating observer previous predictions into updates enables some adaptation, such incremental updates cause accumulative lag that degrades deception. To this end, we propose Deceptive Meta Planning (DeMP), a two-level optimization framework that combines episode-level adaptation, which enables short-term policy adjustment to counter updated observer, and meta-level updates, which leverage cross-episode feedback to capture how observers update their models and accelerate adaptation in future episodes. In this way, DeMP mitigates the accumulation of adaptation lag, enabling sustained deception against a learning observer. Experiments across environments demonstrate that DeMP significantly outperforms existing approaches in RDPP while maintaining competitive path cost. Our results highlight the importance of modeling repeated interactions with learnable adversaries, providing new insights into deception and privacy in multi-agent systems.