Provable Distributional Value Iteration under Partial Observability

📰 ArXiv cs.AI

Learn how to apply Distributional Value Iteration under partial observability to tackle uncertainty in real-world planning tasks

advanced Published 7 May 2026

Action Steps

Extend Distributional Reinforcement Learning (DistRL) to partially observable domains
Model the entire return distribution using latent models
Approximate beliefs about the environment's state
Plan actions using the approximated beliefs and return distribution
Evaluate the performance of the agent under partial observability

Who Needs to Know This

Researchers and engineers working on reinforcement learning and planning under uncertainty can benefit from this article to improve their agents' performance in real-world tasks

Key Insight

💡 Distributional Value Iteration can be extended to partially observable domains to improve planning under uncertainty

Full Article

Title: Provable Distributional Value Iteration under Partial Observability

Abstract:
arXiv:2505.06518v3 Announce Type: replace Abstract: In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes induced by stochastic dynamics and rewards. Motivated by recent progress in world model approaches, where latent models approximate beliefs and support planning, we extend Distributional Reinforcement Learning (DistRL), which models the entire return distribution for fully observable domains, to Partially Observable Ma

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