Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes

arXiv:2609.28792 · cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Reinforcement Learning — 29× in this paper · explained in 4 episodes
• Verification — 12× in this paper · explained in 2 episodes
• Sample Complexity — 11× in this paper · explained in 15 episodes
• International Conference on Machine Learning — 10× in this paper
• Neural Information Processing Systems — 9× in this paper
• Q-learning — 8× in this paper · explained in 3 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Homogeneity — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Regret — 3× in this paper · explained in 3 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Policy iteration algorithm for zero-sum multichain stochastic games with mean payoff and perfect information
• Projection: A Unified Approach to Semi-Infinite Linear Programs and Duality in Convex Programming
• Beyond discounted returns: Robust Markov decision processes with average and Blackwell optimality
• An Efficient Solution to s-Rectangular Robust Markov Decision Processes
• Single-Trajectory Distributionally Robust Reinforcement Learning
• Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
• Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
• Average-Reward Reinforcement Learning with Trust Region Methods
• Model-Free Robust $\phi$-Divergence Reinforcement Learning Using Both Offline and Online Data
• Robust Reinforcement Learning using Offline Data
• Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
• The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
• On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly Communicating MDPs
• On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
• Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
• Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
• Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
• Non-Rectangular Average-Reward Robust MDPs: Optimal Policies and Their Transient Values
• Sample Complexity of Variance-reduced Distributionally Robust Q-learning
• Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning

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