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Guided Policy Optimization under Partial Observability

Yueheng Li, Guangming Xie, Zongqing Lu · May 21, 2025 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

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.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

Human demonstrations

Directly usable for protocol triage.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: Human demonstrations
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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