ARXIV:2603.14600 · REINFORCEMENT LEARNING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study

arXiv

A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

Blocked on Code›Score2.0Evidence unverified

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Pain A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

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PROBLEM

A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability. However, interpreting their internal learning behavior remains a challenge.

METHOD

Reinforcement learning algorithms have been widely used in dynamic and control systems. However, interpreting their internal learning behavior remains a challenge.

Full abstract

Reinforcement learning algorithms have been widely used in dynamic and control systems. However, interpreting their internal learning behavior remains a challenge. In the authors' previous work, a critic match loss landscape visualization method was proposed to study critic training. This study extends that method into a framework which provides a multi-perspective view of the learning dynamics, clarifying how value estimation, policy optimization, and temporal-difference (TD) signals interact during training. The proposed framework includes four complementary components; a three-dimensional reconstruction of the critic match loss surface that shows how TD targets shape the optimization geometry; an actor loss landscape under a frozen critic that reveals how the policy exploits that geometry; a trajectory combining time, Bellman error, and policy weights that indicates how updates move across the surface; and a state-TD map that identifies the state regions that drive those updates. The Action-Dependent Heuristic Dynamic Programming (ADHDP) algorithm for spacecraft attitude control is used as a case study. The framework is applied to compare several ADHDP variants and shows how training stabilizers and target updates change the optimization landscape and affect learning stability. Therefore, the proposed framework provides a systematic and interpretable tool for analyzing reinforcement learning behavior across algorithmic designs.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. The proposed framework includes four complementary components; a three-dimensional reconstruction of the critic match loss surface that shows how TD targets shape the optimization…

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 2.0/10.

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Opportunity summary

Score2.0

PainA framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

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Competitive landscape

A framework for visualizing loss landscapes in reinforcement learning to enhance interpretability.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

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not classified

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not classified

Substitute

not classified

Unknown

not classified

A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study

A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study

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