mlsanityData to 14 Oct 2026

Adversarial Reinforcement Learning Framework for ESP Cheater Simulation

arXiv 2509.24274 ↗First posted 29 Sep 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 28 of its 54 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

28 of 54 references.

References · 28

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  6. 2016Generative Adversarial Imitation Learningcited by 1,597
  7. 2015An Introduction to Convolutional Neural Networkscited by 514
  8. 2017Robust Adversarial Reinforcement Learningcited by 451
  9. 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 328
  10. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  11. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 275
  12. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
16 more
  1. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  2. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 135
  3. 2018The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimizationcited by 101
  4. 2024Self-playing Adversarial Language Game Enhances LLM Reasoningcited by 83
  5. 2021Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLcited by 55
  6. 2018On Gradient-Based Learning in Continuous Gamescited by 48
  7. 2020Reinforcement Learning with Trajectory Feedbackcited by 40
  8. 2018Local Saddle Point Optimization: A Curvature Exploitation Approachcited by 28
  9. 2019Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learningcited by 27
  10. 2014On the Characterization of Local Nash Equilibria in Continuous Gamescited by 22
  11. 2020Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDERcited by 19
  12. 2022Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attackscited by 12
  13. 2019Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competitioncited by 7
  14. 2022GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooterscited by 7
  15. 2021Robust Vision-Based Cheat Detection in Competitive Gamingcited by 4
  16. 2024Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Gamescited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking
  2. 2022Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games
  3. 2026Generalization Hacking: Models Can Game Reinforcement Learning by Preventing Behavioral Generalization
  4. 2026CheatBench: Measuring Reward Gaming in AI Agents
  5. 2026Gradient Routing Localizes and Removes Unintended Behaviors in RL
  6. 2025Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents
  7. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
  8. 2025Auditing Data Membership in Reinforcement Learning With Verifiable Rewards