Robust Partially Observable Markov Decision Processes

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Reward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games
Oct 19, 2026Durgam Latha, Dion Reji, Dorde Zikelic +1Partially Observable Stochastic Games Robust Partially Observable Markov Decision Processes
Indian Institute of Technology Bombay Mumbai, India · Nanyang Technological University Singapore, Singapore
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs
Oct 10, 2026Eline M. Bovy, Caleb Probine, Marnix Suilen +2Robust Partially Observable Markov Decision Processes Partially Observable Markov Decision Processes
Radboud University Nijmegen, The Netherlands · The University of Texas at Austin Austin, USA · University of Antwerp Antwerp, Belgium +1
Solving Robust POMDPs with Omega-regular Objectives via Partially Observable Stochastic Games
Aug 25, 2026Durgam Latha, Dion Reji, S. Akshay +2Robust Markov Decision Processes Partially Observable Stochastic Games
Indian Institute of Technology Bombay Mumbai, India · Nanyang Technological University Singapore, Singapore
Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks
Jul 10, 2026Nuocheng Yang, Sihua Wang, Zihan Chen +2Multi-Agent LLM Systems Multi-Agent Coordination
Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 487372, Singapore
Orchestrated Reality: From Role-Play to Living, Playable Game Worlds -- LLM-Driven World Simulation as a Parameterized-Action POMDP
Jun 14, 2026Yuhang Huang, Chenmiao Li, Chaowei FangLarge Language Model-Based Role-Play Simulation LLM Agent Orchestration
The University of Tokyo, Japan · Individual Researcher, Japan
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
Jun 14, 2026Wenjing Tang, Xuanjin Jin, Yuan Liu +3Belief-Space Planning Symbolic Planning
Shanghai Jiao Tong University · Shanghai Innovation Institute
Multi-Environment POMDPs with Finite-Horizon Objectives
May 8, 2026Léonard Brice, Filip Cano, Krishnendu Chatterjee +2Belief-Space Planning Markov Models
Institute of Science and Technology Austria
Robustness Analysis of POMDP Policies to Observation Perturbations
Apr 23, 2026Benjamin Kraske, Qi Heng Ho, Federico Rossi +2Robust Partially Observable Markov Decision Processes Partially Observable Markov Decision Processes
University of Colorado Boulder, Boulder, Colorado, USA · Virginia Tech, Blacksburg, Virginia, USA · Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
FactorSmith: Agentic Simulation Generation via Markov Decision Process Decomposition with Planner-Designer-Critic Refinement
Mar 15, 2026Ali Shamsaddinlou, Morteza NourelahiAlamdariRobust Partially Observable Markov Decision Processes Program Synthesis

Oct 19, 2026J/K move · Enter open · S save

Reward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games

Durgam Latha, Dion Reji, Dorde Zikelic, Shankaranarayanan Krishna

Indian Institute of Technology Bombay Mumbai, India · Nanyang Technological University Singapore, Singapore

Partially Observable Stochastic Games Robust Partially Observable Markov Decision Processes Robust Markov Decision Processes Partially Observable Markov Decision Processes Risk-Sensitive Planning Risk-Sensitive RL

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Robust partially observable Markov decision processes model agents that act under partial observability while adversarial environment resolves uncertainty in the transition probabilities. Reward objectives such as discounted-sum, long-run average and cumulative reward under finite and infinite horizons allow quantifying the utility of an agent. We consider the problem of solving RPOMDPs under all four reward objectives, where the goal is to decide whether the value of a risk measure with respect to a reward objective exceeds a given threshold. We consider different risk measures, such as (1)~expected value, (2)~mean-variance (MV), (3)~conditional value-at-risk (CVaR), and (4)~entropic risk (ERisk). Our results can be summarized as follows. First, we formalize the class of law-invariant risk measures, that subsume all four risk measures above. Second, we propose a unified framework for solving RPOMDPs with discounted-sum, long-run average and cumulative reward objectives under any law-invariant risk measure, by reducing the problem to solving partially observable stochastic games (POSGs). Our unified framework applies to (s,a)-rectangular RPOMDPs with polytopic uncertainty sets, which we show to be {\em equivalent} to POSGs, for each of the four reward objectives and any law-invariant risk measure. We prove equivalence by designing logspace reductions between the RPOMDP and POSG models, that map strategies to strategies and preserve the distribution of the reward objective, up to a constant rescaling factor. Third, as a corollary, we derive new computational complexity results for R(PO)MDPs and POSGs. In particular, we show that solving (s,a)-rectangular RMDPs is as hard as solving simple stochastic games.