ARXIV:2605.31289 · REINFORCEMENT LEARNING REPRESENTATIONS · SUBMITTED 01 JUN · 20:31 UTC · FRESHNESS STALE

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The Terminal Representation in Reinforcement Learning

Amir Esterhuysen · Anders Jonsson · arXiv

Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead. Two well established approaches are through the successor representation (SR) and the default representation (DR).

METHOD

Full abstract

Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representation (SR) and the default representation (DR). The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward. The DR builds on this by weighting trajectories with reward, integrating credit-assignment structure into the representation. Eigenvectors of both representations have been used to support a range of downstream tasks -- including option discovery, reward shaping, transfer learning, and exploration. We introduce a structurally distinct formulation: the terminal representation (TR). The TR encodes reward-weighted trajectories similarly to the DR, but can be learned as a lower-dimensionality object, and can be used directly for the mentioned applications without eigenvector computations. Eigendecomposition also imposes the assumption of symmetric transition dynamics, which the TR can bypass. In this work we develop the theoretical foundations of the TR: its derivation, convergence of two learning algorithms, its use for zero-shot compositionality, and equivalences between alternative reward formulations. We further show the TR is embedded in the top DR eigenvector, allowing it to capture the same underlying knowledge without eigendecomposition. Additionally, we provide empirical evidence of the TR as a viable alternative to existing representations in subsidiary applications, while requiring less computational overhead to learn, store, and use.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Eigenvectors of both representations have been used to support a range of downstream tasks -- including option discovery, reward shaping, transfer learning, and exploration.

WHY NOW

Reinforcement Learning Representations moved forward this cycle; last verified June 2026. Public score 3.0/10.

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Opportunity summary

Score3.0

PainIntroduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

Segment

Reinforcement Learning Representations

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

Introduces the Terminal Representation (TR) for reinforcement learning, offering a lower-dimensionality alternative to existing representations with reduced computational overhead.

Segment

Reinforcement Learning Representations

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

The Terminal Representation in Reinforcement Learning

The Terminal Representation in Reinforcement Learning

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