Adversarial Reinforcement Learning Framework for ESP Cheater Simulation

2025-09-29

AI Summary

In online games, extrasensory perception (ESP) cheating poses severe challenges for anti-cheat systems due to its difficulty in ground-truth labeling, high camouflage capability, and consequent scarcity of annotated training data. Method: We propose a controllable two-agent reinforcement learning game framework. Specifically, we design a structured risk-aware cheater model that dynamically balances reward gain against detection risk and adaptively switches ESP behaviors; further, we formulate cheating and detection as a co-evolutionary adversarial process, incorporating observation-bias-aware behavioral modeling and trajectory classification, enhanced by adversarial training to emulate realistic confrontation dynamics. Contribution/Results: Our framework generates high-reward, low-detectability adaptive ESP cheating policies. It provides a scalable simulation platform and high-fidelity synthetic training data for anti-cheat systems, significantly improving detector generalization under label-scarce conditions.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in player behavior. The lack of observable evidence makes it difficult to collect reliably labeled data, which is essential for training effective anti-cheat systems. Furthermore, cheaters often adapt their behavior by limiting or disguising their cheat usage, which further complicates detection and detector development. To address these challenges, we propose a simulation framework for controlled modeling of ESP cheaters, non-cheaters, and trajectory-based detectors. We model cheaters and non-cheaters as reinforcement learning agents with different levels of observability, while detectors classify their behavioral trajectories. Next, we formulate the interaction between the cheater and the detector as an adversarial game, allowing both players to co-adapt over time. To reflect realistic cheater strategies, we introduce a structured cheater model that dynamically switches between cheating and non-cheating behaviors based on detection risk. Experiments demonstrate that our framework successfully simulates adaptive cheater behaviors that strategically balance reward optimization and detection evasion. This work provides a controllable and extensible platform for studying adaptive cheating behaviors and developing effective cheat detectors.

Problem

• Simulating ESP cheaters with adaptive behavior strategies
• Addressing lack of observable evidence for cheat detection
• Modeling adversarial interactions between cheaters and detectors

Innovation

• Simulates ESP cheaters using reinforcement learning agents
• Models cheater-detector interaction as adversarial co-adaptation game
• Introduces dynamic behavior switching based on detection risk