OpenNovelty.org

Guided Policy Optimization under Partial Observability

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
reinforcement learningteacher-student learningpolicy distillationPOMDPspolicy gradient
Abstract:

Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem. To address this, we introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner. The guider takes advantage of privileged information while ensuring alignment with the learner's policy that is primarily trained via imitation learning. We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches. Empirical evaluations show strong performance of GPO across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.

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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.
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Overview

Overall Novelty Assessment

The paper introduces Guided Policy Optimization (GPO), a framework co-training a privileged guider and a learner policy through imitation learning alignment. It resides in the Teacher-Student Distillation Approaches leaf, which contains seven papers addressing how privileged teacher policies guide student policies under partial observability. This leaf sits within the broader Privileged Information Utilization Frameworks branch, indicating a moderately populated research direction focused on leveraging training-time state information to overcome deployment-time observability constraints.

The taxonomy reveals neighboring approaches in sibling leaves: Asymmetric Actor-Critic Architectures (four papers) employ critics with privileged access while actors remain partially observable, and specialized applications in Robotic Manipulation (three papers) and Autonomous Navigation (four papers) apply privileged training to domain-specific tasks. GPO's distillation-based design contrasts with asymmetric critic methods and shares conceptual ground with sibling works exploring when distillation succeeds under partial observability, though the taxonomy scope notes exclude auxiliary task methods and asymmetric critic designs from this leaf.

Among twenty-four candidates examined, the GPO framework contribution shows one refutable candidate out of ten examined, suggesting some prior overlap in co-training schemes. The theoretical optimality guarantee similarly identifies one refutable candidate among four examined, indicating existing theoretical work on privileged learning convergence. The two practical variants (GPO-penalty and GPO-clip) found no refutable candidates across ten examined papers, appearing more novel within this limited search scope. The analysis reflects top-K semantic retrieval plus citation expansion, not exhaustive coverage of all distillation or privileged learning literature.

Given the limited search scope of twenty-four candidates, the framework appears to build on established teacher-student paradigms with incremental theoretical and algorithmic refinements. The taxonomy position in a seven-paper leaf suggests moderate prior activity in distillation-based privileged learning, though the specific combination of co-training dynamics and optimality guarantees may offer distinguishing features. The analysis cannot assess novelty against the full corpus of privileged RL or imitation learning work beyond the examined candidates.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
24
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Reinforcement learning in partially observable environments with privileged information. The field addresses scenarios where agents must act under limited observations but can access richer state information during training. The taxonomy reveals several major branches: Privileged Information Utilization Frameworks focus on teacher-student distillation and asymmetric training schemes (e.g., Asymmetric DQN[10], Fully Observable Policies[15]); Belief and History Representation Learning tackles how agents encode past observations into useful internal states (e.g., Belief Grounded Networks[17], Complementary Past Representations[30]); World Model-Based Approaches learn predictive models to compensate for partial observability (e.g., State Space World Models[13], PIGDreamer[37]); Multi-Agent Coordination and Domain-Specific Applications address collaborative settings and specialized tasks like robotics or UAV navigation (e.g., UAV Privileged Information[2], Robot Soccer Egocentric[28]); while Meta-Learning, Attention Mechanisms, and Foundation Model Integration explore adaptive inference, structured inductive biases, and large-scale pretraining. Recent work has intensified around distillation strategies that transfer knowledge from privileged teachers to deployable students, balancing sample efficiency against deployment constraints. Guided Policy Optimization[0] sits squarely within the Teacher-Student Distillation Approaches, emphasizing how to effectively guide student policies using privileged state information available only during training. It shares methodological kinship with Distill or Decide[38] and Distilling Realizable Students[43], which similarly explore when and how distillation succeeds under partial observability. In contrast, nearby works like Provable Privileged Information[32] offer theoretical guarantees, while Privileged Training Frameworks[46] propose broader architectural patterns. The central tension across these lines involves trading off the richness of privileged supervision against the robustness and generalization of the final partially observable policy, with open questions around optimal distillation objectives and the role of auxiliary tasks in bridging the observability gap.

Claimed Contributions

Guided Policy Optimization (GPO) framework

The authors propose GPO, a novel framework that simultaneously trains two entities: a guider with access to privileged information and a learner operating under partial observability. The guider provides supervision while being constrained to remain aligned with the learner's policy through a backtracking mechanism, ensuring the learner can effectively imitate the guider.

10 retrieved papers
Can Refute
Theoretical optimality guarantee for GPO

The authors provide theoretical analysis showing that GPO's learner update can be viewed as constrained policy mirror descent, achieving optimality comparable to direct reinforcement learning. This addresses fundamental limitations in teacher-student learning such as the imitation gap and suboptimality from inimitable teachers.

4 retrieved papers
Can Refute
Two practical GPO variants: GPO-penalty and GPO-clip

The authors develop two concrete implementations of the GPO framework. GPO-penalty uses an adaptive KL-divergence penalty to maintain alignment between guider and learner, while GPO-clip employs a double-clip mechanism and selective masking to prevent the guider from diverging too far while avoiding unnecessary backtracking.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Guided Policy Optimization (GPO) framework

The authors propose GPO, a novel framework that simultaneously trains two entities: a guider with access to privileged information and a learner operating under partial observability. The guider provides supervision while being constrained to remain aligned with the learner's policy through a backtracking mechanism, ensuring the learner can effectively imitate the guider.

Contribution

Theoretical optimality guarantee for GPO

The authors provide theoretical analysis showing that GPO's learner update can be viewed as constrained policy mirror descent, achieving optimality comparable to direct reinforcement learning. This addresses fundamental limitations in teacher-student learning such as the imitation gap and suboptimality from inimitable teachers.

Contribution

Two practical GPO variants: GPO-penalty and GPO-clip

The authors develop two concrete implementations of the GPO framework. GPO-penalty uses an adaptive KL-divergence penalty to maintain alignment between guider and learner, while GPO-clip employs a double-clip mechanism and selective masking to prevent the guider from diverging too far while avoiding unnecessary backtracking.

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