SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems

arXiv:2402.03741 · cs.LG, cs.AI, cs.CR · Submitted 2024-02-06 · Read on arXiv

cs.LG, cs.AI, cs.CR

Submitted: 2024-02-06

Updated: 2026-08-31

Comments: To appear in the ACM Conference on Computer and Communications Security (CCS'24), October 14-18, 2024, Salt Lake City, UT, USA

Code: https://github.com/maoubo/SUB-PLAY

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 44× in this paper · explained in 4 episodes
• Occupancy Rate — 33× in this paper
• Transition Dissemination — 23× in this paper
• Subgame Construction — 17× in this paper
• Deep Reinforcement Learning — 10× in this paper · explained in 2 episodes
• Dynamic Observation — 8× in this paper
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Nash Equilibrium — 8× in this paper · explained in 2 episodes
• Transferability — 8× in this paper · explained in 4 episodes
• Policy Meritocracy — 6× in this paper
• Static Estimation — 6× in this paper
• Static Observation — 6× in this paper
• Action Space — 5× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Actor — 4× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Adversarial Attacks — 3× in this paper · explained in 1 episode
• Adversarial Examples — 3× in this paper · explained in 2 episodes
• Catch Rate — 3× in this paper
• Collision Frequency — 3× in this paper
• Cooperation — 3× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Non-stationarity — 2× in this paper · explained in 2 episodes
• Q-learning — 2× in this paper · explained in 3 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Centralized Training Decentralized Execution — 1× in this paper
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Dissemination Rate — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Distributed Training Decentralized Execution — 1× in this paper
• Embodied AI — 1× in this paper · explained in 2 episodes
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Multi Particle Environments — 1× in this paper
• Multi-agent system (MAS) — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes

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