# New Challenges in Reinforcement Learning: A Survey of Security and Privacy

arXiv: https://arxiv.org/abs/2301.00188 · HTML: https://mlsanity.com/paper/2301.00188/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 91 references that are in the map.
- First posted on arXiv 31 Dec 2022.

## Builds on (most cited first, 30 of 42)

- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks](https://mlsanity.com/paper/1701.04143/) (2017)
- [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12909/) (2020)
- [Virtual to Real Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1704.03952/) (2017)
- [Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning](https://mlsanity.com/paper/2005.07099/) (2020)
- [Private Reinforcement Learning with PAC and Regret Guarantees](https://mlsanity.com/paper/2009.09052/) (2020)
- [Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals](https://mlsanity.com/paper/1906.10571/) (2019)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
- [On the Robustness of Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.03722/) (2020)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [On the Complexity of Solving Markov Decision Problems](https://mlsanity.com/paper/1302.4971/) (2013)
- [Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces](https://mlsanity.com/paper/1901.10634/) (2019)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.11082/) (2019)
- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Inverse reinforcement learning for video games](https://mlsanity.com/paper/1810.10593/) (2018)
- [Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1909.02583/) (2019)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks](https://mlsanity.com/paper/2011.10824/) (2020)
- [More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence](https://mlsanity.com/paper/2008.01916/) (2020)

## Built on by (most cited first, 7 of 7)

- [SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity](https://mlsanity.com/paper/2602.08690/) (2026)
- [Strategizing against Q-learners: A Control-theoretical Approach](https://mlsanity.com/paper/2403.08906/) (2024)
- [Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training](https://mlsanity.com/paper/2405.13445/) (2024)
- [AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development](https://mlsanity.com/paper/2411.14442/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [Randomized Least Squares Value Iteration itself is Joint Differentially Private](https://mlsanity.com/paper/2606.01952/) (2026)
- [Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN](https://mlsanity.com/paper/2606.26123/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.