Fetching the paper…
Reading the bibliography…
Cooperative multi-agent reinforcement learning (cMARL) has many real applications, but the policy trained by existing cMARL algorithms is not robust enough when deployed.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Earlier work this paper cites.
Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Value-decomposition networks for cooperative multi-agent learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2017
Earlier work this paper cites.
Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
Earlier work this paper cites.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Earlier work this paper cites.
Sequential attacks on agents for long-term adversarial goals
Edgar Tretschk, Seong Joon Oh, and Mario Fritz · 2018
Earlier work this paper cites.
Wasserstein robust reinforcement learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, and Jun Wang · 2019
Earlier work this paper cites.
Adversarial exploitation of policy imitation
Vahid Behzadan and William Hsu · 2019
Earlier work this paper cites.
Targeted attacks on deep reinforcement learning agents through adversarial observations
Léonard Hussenot, Matthieu Geist, and Olivier Pietquin · 2019
Cited alongside, same era.
Spatiotemporally constrained action space attacks on deep reinforcement learning agents
Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, and Soumik Sarkar · 2019
Cited alongside, same era.
Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell · 2019
Cited alongside, same era.
Robust reinforcement learning for continuous control with model misspecification
Daniel J Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, and Martin Riedmiller · 2019
Cited alongside, same era.
Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson · 2020
Later among the works it cites.
Stealthy and efficient adversarial attacks against deep reinforcement learning
Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, and Yang Liu · 2020
Later among the works it cites.
Roma: Multi-agent reinforcement learning with emergent roles
Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang · 2020
Later among the works it cites.
Enhanced adversarial strategically-timed attacks against deep reinforcement learning
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, and Xiaoli Ma · 2020
Later among the works it cites.
Robust multi-agent reinforcement learning with model uncertainty
Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, and Tamer Basar · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yasar Sinan Nasir and Dongning Guo · 2019
Cited alongside, same era.
Minimalistic attacks: How little it takes to fool a deep reinforcement learning policy
Xinghua Qu, Zhu Sun, Pengfei Wei, Yew-Soon Ong, and Abhishek Gupta · 2019
Cited alongside, same era.
Optimal attacks on reinforcement learning policies
Alessio Russo and Alexandre Proutiere · 2019
Cited alongside, same era.
The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
Cited alongside, same era.
Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
Cited alongside, same era.
Characterizing attacks on deep reinforcement learning
Chaowei Xiao, Xinlei Pan, Warren He, Jian Peng, Mingjie Sun, Jinfeng Yi, Mingyan Liu, Bo Li, and Dawn Song · 2019
Cited alongside, same era.
On the robustness of cooperative multi-agent reinforcement learning
Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, and Nicolas Papernot · 2020
Cited alongside, same era.
Robust constrained reinforcement learning for continuous control with model misspecification
Daniel J Mankowitz, Dan A Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin Riedmiller, and Timothy Mann · 2020
Cited alongside, same era.
Later among the works it cites.
Robust multi-agent q-learning in cooperative games with adversaries
Eleni Nisioti, Daan Bloembergen, and Michael Kaisers · 2021
Later among the works it cites.
Strategically-timed state-observation attacks on deep reinforcement learning agents
You Qiaoben, Xinning Zhou, Chengyang Ying, and Jun Zhu · 2021
Later among the works it cites.
Romax: Certifiably robust deep multiagent reinforcement learning via convex relaxation
Chuangchuang Sun, Dong-Ki Kim, and Jonathan P How · 2021
Later among the works it cites.
Adaptive traffic signal control for large-scale scenario with cooperative group-based multi-agent reinforcement learning
Tong Wang, Jiahua Cao, and Azhar Hussain · 2021
Later among the works it cites.
Robust reinforcement learning under model misspecification
Lebin Yu, Jian Wang, and Xudong Zhang · 2021
Later among the works it cites.
Towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning
Jun Guo, Yonghong Chen, Yihang Hao, Zixin Yin, Yin Yu, and Simin Li · 2022
Closest in time.
Evaluating robustness of cooperative marl: A model-based approach
Nhan H Pham, Lam M Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, and Tsui-Wei Weng · 2022
Closest in time.