OpenNovelty.org

Solving General-Utility Markov Decision Processes in the Single-Trial Regime with Online Planning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Reinforcement learningGeneral-utility Markov decision processesconvex reinforcement learningmarkov decision processes
Abstract:

In this work, we contribute the first approach to solve infinite-horizon discounted general-utility Markov decision processes (GUMDPs) in the single-trial regime, i.e., when the agent's performance is evaluated based on a single trajectory. First, we provide some fundamental results regarding policy optimization in the single-trial regime, investigating which class of policies suffices for optimality, casting our problem as a particular MDP that is equivalent to our original problem, as well as studying the computational hardness of policy optimization in the single-trial regime. Second, we show how we can leverage online planning techniques, in particular a Monte-Carlo tree search algorithm, to solve GUMDPs in the single-trial regime. Third, we provide experimental results showcasing the superior performance of our approach in comparison to relevant baselines.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces an approach for solving infinite-horizon discounted general-utility MDPs evaluated on single trajectories, combining non-standard utility functions with stringent data constraints. According to the taxonomy tree, this work occupies the 'Single-Trial General-Utility MDP Planning' leaf under 'General-Utility and Constrained MDP Optimization'. Notably, this leaf contains only the original paper itself—no sibling papers appear in the same node—indicating that this precise intersection of single-trial evaluation and general-utility objectives represents a sparse research direction within the surveyed literature.

The taxonomy reveals related but distinct directions nearby: 'Constrained MDP Policy Optimization' addresses utility constraints via primal-dual methods but typically assumes multi-trial settings, while 'Occupancy-Based and Scalable General-Utility Methods' handle general utilities with sample complexity guarantees yet do not focus on single-trial constraints. Under 'Single-Trajectory Learning Algorithms', sibling leaves such as 'Value-Function-Based Single-Trajectory Methods' and 'Distributionally Robust Single-Trajectory RL' tackle data scarcity but assume standard reward objectives. The paper bridges these communities by merging general-utility optimization with single-trajectory planning, a combination not directly covered by existing taxonomy nodes.

Among the three contributions analyzed from 24 candidate papers, 'Fundamental results for policy optimization in the single-trial regime' examined 4 candidates and found 1 potentially refutable overlap, suggesting that basic theoretical characterizations may have some prior coverage. In contrast, 'Monte-Carlo tree search algorithm for single-trial GUMDPs' and 'First approach to solve infinite-horizon discounted GUMDPs in single-trial regime' each examined 10 candidates with zero refutable overlaps, indicating that the algorithmic and problem-formulation aspects appear more novel within this limited search scope. The statistics suggest the core methodological and application-level claims face less direct competition in the examined literature.

Given the constrained search scale—24 candidates from top-K semantic matches and citation expansion—this analysis captures the immediate neighborhood rather than an exhaustive survey. The absence of sibling papers in the same taxonomy leaf and the low refutation counts for algorithmic contributions suggest the work occupies a relatively unexplored niche at the intersection of single-trial constraints and general utilities. However, broader searches or domain-specific venues might reveal additional relevant work not surfaced here.

Taxonomy

Core-task Taxonomy Papers
18
3
Claimed Contributions
24
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: single-trial policy optimization in general-utility Markov decision processes. The field addresses how to learn effective policies when only a single trajectory is available and when the objective extends beyond standard expected cumulative reward. The taxonomy reveals several complementary perspectives: Single-Trajectory Learning Algorithms focus on data-efficient methods that extract maximal information from one rollout, while General-Utility and Constrained MDP Optimization tackles non-standard objectives such as risk-sensitive criteria, constraints, or prospect-theoretic utilities. Policy Gradient and Multi-Agent Methods explore gradient-based and distributed optimization, Representation and Structure Learning in MDPs examines how to discover useful state abstractions or exploit problem structure, and Navigation and Optimal Policy Identification as well as Robotic Manipulation Applications ground these ideas in concrete domains. Foundational RL Concepts and Sequential Learning provides the theoretical bedrock, connecting classical results to modern challenges in sample-starved or non-standard settings. Within this landscape, a particularly active line of work investigates how to handle general utility functions and constraints when data is scarce. For instance, Scalable General Utility RL[5] and Prospect Theoretic Policy Gradient[12] illustrate recent efforts to optimize beyond expected return, while Single Trajectory Robust RL[4] and Single Markovian Trajectory[18] emphasize robustness and sample efficiency from minimal data. General Utility Online Planning[0] sits squarely in this intersection, addressing the challenge of planning under general utilities when only one trial is available. Its emphasis on online planning distinguishes it from offline or batch approaches such as Primal Dual Actor Critic[2], which typically assume richer data or iterative policy updates. By targeting single-trial scenarios with non-standard objectives, General Utility Online Planning[0] complements works like Scalable General Utility RL[5] that scale to larger problems but may rely on more extensive sampling, highlighting an ongoing trade-off between sample complexity and the breadth of utility functions one can handle.

Claimed Contributions

Fundamental results for policy optimization in the single-trial regime

The authors establish theoretical foundations for solving GUMDPs in the single-trial setting. They prove that non-Markovian policies are necessary for optimality, introduce an occupancy MDP formulation that is equivalent to the original problem, and demonstrate that policy optimization in this regime is NP-Hard even for smooth convex objectives.

4 retrieved papers
Can Refute
Monte-Carlo tree search algorithm for single-trial GUMDPs

The authors develop an MCTS-based online planning algorithm that solves the occupancy MDP formulation. The algorithm provably converges to the optimal action at each timestep given sufficient iterations and achieves polynomial regret bounds.

10 retrieved papers
First approach to solve infinite-horizon discounted GUMDPs in single-trial regime

The authors present the first method for solving infinite-horizon discounted GUMDPs when performance is evaluated on a single trajectory rather than over infinite trials. This addresses a practical limitation where optimal policies for the infinite-trial formulation may perform poorly when evaluated on limited trajectories.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Fundamental results for policy optimization in the single-trial regime

The authors establish theoretical foundations for solving GUMDPs in the single-trial setting. They prove that non-Markovian policies are necessary for optimality, introduce an occupancy MDP formulation that is equivalent to the original problem, and demonstrate that policy optimization in this regime is NP-Hard even for smooth convex objectives.

Contribution

Monte-Carlo tree search algorithm for single-trial GUMDPs

The authors develop an MCTS-based online planning algorithm that solves the occupancy MDP formulation. The algorithm provably converges to the optimal action at each timestep given sufficient iterations and achieves polynomial regret bounds.

Contribution

First approach to solve infinite-horizon discounted GUMDPs in single-trial regime

The authors present the first method for solving infinite-horizon discounted GUMDPs when performance is evaluated on a single trajectory rather than over infinite trials. This addresses a practical limitation where optimal policies for the infinite-trial formulation may perform poorly when evaluated on limited trajectories.

Table of Contents