# Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games

arXiv: https://arxiv.org/abs/2510.07813 · HTML: https://mlsanity.com/paper/2510.07813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Agent Modelling under Partial Observability for Deep Reinforcement Learning](https://mlsanity.com/paper/2006.09447/) (2020)
- [HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation](https://mlsanity.com/paper/2109.05490/) (2021)
- [Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces](https://mlsanity.com/paper/1905.04388/) (2019)
- [Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.08193/) (2020)
- [An Introduction to Pursuit-evasion Differential Games](https://mlsanity.com/paper/2003.05013/) (2020)
- [Cooperative Team Strategies for Multi-player Perimeter-Defense Games](https://mlsanity.com/paper/1912.04342/) (2019)
- [MERL: Multi-Head Reinforcement Learning](https://mlsanity.com/paper/1909.11939/) (2019)
- [A Pursuit-Evasion Differential Game with Strategic Information Acquisition](https://mlsanity.com/paper/2102.05469/) (2021)
- [Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay](https://mlsanity.com/paper/2202.00329/) (2022)
- [Multi-Robot Target Tracking with Sensing and Communication Danger Zones](https://mlsanity.com/paper/2404.07880/) (2024)
- [Common Information based Markov Perfect Equilibria for Linear-Gaussian Games with Asymmetric Information](https://mlsanity.com/paper/1401.4786/) (2014)
- [Self-triggered Pursuit of a Single Evader with Uncertain Information](https://mlsanity.com/paper/1512.06184/) (2015)
- [Linear Quadratic Games with Costly Measurements](https://mlsanity.com/paper/1709.06910/) (2017)
- [Efficient Communication for Pursuit-Evasion Games with Asymmetric Information](https://mlsanity.com/paper/2307.01305/) (2023)
- [Optimal Evasion from a Sensing-Limited Pursuer](https://mlsanity.com/paper/2401.12848/) (2024)

## Abstract (arXiv)

Adversarial environments require agents to navigate a key strategic trade-off: acquiring information enhances situational awareness, but may simultaneously expose them to threats. To investigate this tension, we formulate a PursuitEvasion-Exposure-Concealment Game (PEEC) in which a pursuer agent must decide when to communicate in order to obtain the evader's position. Each communication reveals the pursuer's location, increasing the risk of being targeted. Both agents learn their movement policies via reinforcement learning, while the pursuer additionally learns a communication policy that balances observability and risk. We propose SHADOW (Strategic-communication Hybrid Action Decision-making under partial Observation for Warfare), a multi-headed sequential reinforcement learning framework that integrates continuous navigation control, discrete communication actions, and opponent modeling for behavior prediction. Empirical evaluations show that SHADOW pursuers achieve higher success rates than six competitive baselines. Our ablation study confirms that temporal sequence modeling and opponent modeling are critical for effective decision-making. Finally, our sensitivity analysis reveals that the learned policies generalize well across varying communication risks and physical asymmetries between agents.