Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective

arXiv:2609.24103 · cs.LG, cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: Accepted to the RLC 2025 Finding the Frame Workshop

Code: https://github.com/lpreuettUW/distributional_point_based_value_iteration

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

Terminology

• Reinforcement Learning — 17× in this paper · explained in 4 episodes
• Point-Based Value Iteration — 7× in this paper
• Reward Function — 6× in this paper · explained in 2 episodes
• Wasserstein Distance — 6× in this paper · explained in 4 episodes
• Distributional Point-Based Value Iteration — 4× in this paper
• Action Space — 2× in this paper · explained in 1 episode
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Higher-order statistics — 1× in this paper · explained in 2 episodes
• Partially Observable Markov Decision Process — 1× in this paper
• Partially observable Markov decision process (POMDP) — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

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