Learning Chance-Constrained MDPs with Bellman Distributional Certificates

arXiv:2609.30856 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Reinforcement Learning — 20× in this paper · explained in 4 episodes
• Sample Complexity — 11× in this paper · explained in 15 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Post-Selection — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Decision and Control — 1× in this paper
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Primal-Dual Sample Complexity Bounds for Constrained Markov Decision Processes with Multiple Constraints
• Exploration-Exploitation in Constrained MDPs
• Stochastic Recursive Momentum for Policy Gradient Methods

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