Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games

arXiv:2511.11602 · cs.LG, cs.GT, cs.MA, math.OC · Submitted 2025-10-31 · Read on arXiv

cs.LG, cs.GT, cs.MA, math.OC

Submitted: 2025-10-31

Updated: 2026-08-28

Terminology

• Q-learning — 15× in this paper · explained in 3 episodes
• Aspiration-based Perturbed Learning Automata — 10× in this paper
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Convergence Guarantees — 5× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Cooperation — 3× in this paper · explained in 2 episodes
• Multi-Agent System — 3× in this paper · explained in 6 episodes
• Nash Equilibrium — 3× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part B: Stochastic Stability in Weakly Acyclic Games

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks