Robust PAC Learning of Concurrent Stochastic Games

arXiv:2609.04189 · cs.LG, cs.GT, cs.LO, cs.MA · Submitted 2026-09-03 · Read on arXiv

cs.LG, cs.GT, cs.LO, cs.MA

Submitted: 2026-09-03

Updated: 2026-09-03

Terminology

• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Sample Complexity — 18× in this paper · explained in 15 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Regret — 6× in this paper · explained in 3 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Nash Equilibrium — 4× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Formal Methods — 2× in this paper · explained in 3 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Probably Approximately Correct — 2× in this paper
• Q-learning — 2× in this paper · explained in 3 episodes
• Sample Complexity Scaling — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Minimax Regret — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Distributionally Robust Markov Games with Average Reward

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