# Deep Reinforcement Learning for Green Security Games with Real-Time Information

arXiv: https://arxiv.org/abs/1811.02483 · HTML: https://mlsanity.com/paper/1811.02483/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 19 of 19)

- [Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization](https://mlsanity.com/paper/2103.04564/) (2021)
- [Robust Reinforcement Learning Under Minimax Regret for Green Security](https://mlsanity.com/paper/2106.08413/) (2021)
- [Policy Space Response Oracles: A Survey](https://mlsanity.com/paper/2403.02227/) (2024)
- [Adversarial Deep Learning for Online Resource Allocation](https://mlsanity.com/paper/2111.10285/) (2021)
- [Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning](https://mlsanity.com/paper/2302.00797/) (2023)
- [Automating Privilege Escalation with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.01362/) (2021)
- [NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search](https://mlsanity.com/paper/2201.07224/) (2022)
- [Carbon Market Simulation with Adaptive Mechanism Design](https://mlsanity.com/paper/2406.07875/) (2024)
- [Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.01163/) (2022)
- [Power and accountability in reinforcement learning applications to environmental policy](https://mlsanity.com/paper/2205.10911/) (2022)
- [Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games](https://mlsanity.com/paper/2212.14431/) (2022)
- [Multi-defender Security Games with Schedules](https://mlsanity.com/paper/2311.16392/) (2023)
- [Self-adaptive PSRO: Towards an Automatic Population-based Game Solver](https://mlsanity.com/paper/2404.11144/) (2024)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information](https://mlsanity.com/paper/2211.04987/) (2022)
- [Targets in Reinforcement Learning to solve Stackelberg Security Games](https://mlsanity.com/paper/2211.17132/) (2022)
- [Memory Assignment for Finite-Memory Strategies in Adversarial Patrolling Games](https://mlsanity.com/paper/2505.14137/) (2025)
- [PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense](https://mlsanity.com/paper/2508.19488/) (2025)
- [Global Policy-Space Response Oracles for Two-Player Zero-Sum Games](https://mlsanity.com/paper/2605.28273/) (2026)

## Abstract (arXiv)

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents' subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.