Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization | Paper Citation Record

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This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Observation 0c629fa2-b676-4ebf-a403-7d3f06ebde26 · outbound

This paper cites On the estimation of production frontiers: maximum likelihood estimation of the parameters of a discontinuous density function.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization On the estimation of production frontiers: maximum likelihood estimation of the parameters of a discontinuous density function

Reference 2

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This paper cites Hindsight experience replay.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Hindsight experience replay

Reference 3

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This paper cites Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint

Reference 4

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This paper cites Accelerating goal-conditioned reinforcement learning algorithms and research.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Accelerating goal-conditioned reinforcement learning algorithms and research

Reference 5

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This paper cites Offline rl without off-policy evaluation.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Offline rl without off-policy evaluation

Reference 6

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This paper cites When does return-conditioned supervised learning work for offline reinforcement learning? Advances in Neural Information Processing Systems, 35: 0 1542--1553, 2022.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization When does return-conditioned supervised learning work for offline reinforcement learning? Advances in Neural Information Processing Systems, 35: 0 1542--1553, 2022

Reference 7

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Observation 1900dc52-3d95-4889-bea5-aa5ba811b9a9 · outbound

This paper cites Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning

Reference 8

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Observation da09bc93-76e4-4615-9e8e-9c8fe5094a62 · outbound

This paper cites Goal-conditioned reinforcement learning with imagined subgoals.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Goal-conditioned reinforcement learning with imagined subgoals

Reference 9

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This paper cites On the statistical benefits of temporal difference learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization On the statistical benefits of temporal difference learning

Reference 10

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Observation 05f055f6-a886-4d96-9ed4-26e1783a705a · outbound

This paper cites Decision transformer: Reinforcement learning via sequence modeling.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Decision transformer: Reinforcement learning via sequence modeling

Reference 11

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This paper cites Goal-conditioned imitation learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Goal-conditioned imitation learning

Reference 12

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Observation b26c610a-459b-44af-9acb-69a25dc55670 · outbound

This paper cites RvS: What is Essential for Offline RL via Supervised Learning?.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization RvS: What is Essential for Offline RL via Supervised Learning?

Reference 13

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Observation bbad15d8-706f-4444-9661-dcec1aa5946d · outbound

This paper cites C-Learning: Learning to Achieve Goals via Recursive Classification.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization C-Learning: Learning to Achieve Goals via Recursive Classification

Reference 14

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Observation 6ffaf888-2f16-4a8a-8ffd-9d2ed56273b1 · outbound

This paper cites Imitating past successes can be very suboptimal.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Imitating past successes can be very suboptimal

Reference 15

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This paper cites Contrastive learning as goal-conditioned reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Contrastive learning as goal-conditioned reinforcement learning

Reference 16

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Observation 8408698a-ec22-445f-8d72-8d4e4f62cfc2 · outbound

This paper cites Inference via interpolation: Contrastive representations provably enable planning and inference.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Inference via interpolation: Contrastive representations provably enable planning and inference

Reference 17

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Observation 4e9db73b-ece7-43c6-9a15-e5a3a1b6028b · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 18

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Observation 6833a941-aa87-4b87-b910-42a1c54cfb00 · outbound

This paper cites A minimalist approach to offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization A minimalist approach to offline reinforcement learning

Reference 19

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This paper cites Learning to reach goals via iterated supervised learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning to reach goals via iterated supervised learning

Reference 20

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This paper cites Closing the gap between TD learning and supervised learning - a generalisation point of view.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Closing the gap between TD learning and supervised learning - a generalisation point of view

Reference 21

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Observation 36e41fa6-edff-4f22-af06-65760ef38fe7 · outbound

This paper cites Distance Weighted Supervised Learning for Offline Interaction Data.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Distance Weighted Supervised Learning for Offline Interaction Data

Reference 22

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Diffused task-agnostic milestone planner

Reference 23

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This paper cites Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling

Reference 24

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This paper cites Learning to reach goals via diffusion.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning to reach goals via diffusion

Reference 25

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This paper cites Efficient planning in a compact latent action space.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Efficient planning in a compact latent action space

Reference 26

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This paper cites Adaptive q -aid for conditional supervised learning in offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Adaptive q -aid for conditional supervised learning in offline reinforcement learning

Reference 27

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This paper cites Imitating Graph-Based Planning with Goal-Conditioned Policies.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Imitating Graph-Based Planning with Goal-Conditioned Policies

Reference 28

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This paper cites Adam: A method for stochastic optimization.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Adam: A method for stochastic optimization

Reference 29

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization An Introduction to Variational Autoencoders

Reference 30

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Offline Reinforcement Learning with Implicit Q-Learning

Reference 31

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Stabilizing off-policy q-learning via bootstrapping error reduction

Reference 32

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Conservative q-learning for offline reinforcement learning

Reference 33

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Multi-game decision transformers

Reference 34

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Dhrl: a graph-based approach for long-horizon and sparse hierarchical reinforcement learning

Reference 35

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This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 36

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Hierarchical planning through goal-conditioned offline reinforcement learning

Reference 37

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation

Reference 38

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This paper cites Beyond ood state actions: Supported cross-domain offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Beyond ood state actions: Supported cross-domain offline reinforcement learning

Reference 39

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Least squares quantization in pcm

Reference 40

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Decoupled Weight Decay Regularization

Reference 41

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Generative Trajectory Stitching through Diffusion Composition

Reference 42

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Decision mamba: A multi-grained state space model with self-evolution regularization for offline rl

Reference 43

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning latent plans from play

Reference 44

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This paper cites How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression

Reference 45

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization VIP : Towards universal visual reward and representation via value-implicit pre-training

Reference 46

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Human-level control through deep reinforcement learning

Reference 47

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making

Reference 48

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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Asymmetric least squares estimation and testing

Reference 49

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This paper cites Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces

Reference 50

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This paper cites Hiql: Offline goal-conditioned rl with latent states as actions.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Hiql: Offline goal-conditioned rl with latent states as actions

Reference 51

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This paper cites Foundation Policies with Hilbert Representations.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Foundation Policies with Hilbert Representations

Reference 52

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This paper cites Ogbench: Benchmarking offline goal-conditioned rl.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Ogbench: Benchmarking offline goal-conditioned rl

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.739722Z

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Observation 03cde593-9e7a-4b38-b8c0-d2b09ffe2fe9 · outbound

This paper cites A survey on offline reinforcement learning: Taxonomy, review, and open problems.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization A survey on offline reinforcement learning: Taxonomy, review, and open problems

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Observation 738e246b-af48-488e-bd3d-3f9c6928ce3d · outbound

This paper cites Goal-Conditioned Imitation Learning using Score-based Diffusion Policies.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Goal-Conditioned Imitation Learning using Score-based Diffusion Policies

Reference 55

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Observation 0047489f-1d2b-4ace-91ba-3d824f4a0da4 · outbound

This paper cites Stochastic backpropagation and approximate inference in deep generative models.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Stochastic backpropagation and approximate inference in deep generative models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.716004Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f43cd76c-e99b-47ef-a3bc-6bd55c4b6680 · outbound

This paper cites Outcome-driven reinforcement learning via variational inference.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Outcome-driven reinforcement learning via variational inference

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.704287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5d75398f-09e4-484f-a1a0-d8b10e8421f6 · outbound

This paper cites Reinforcement learning upside down: Don't predict rewards -- just map them to actions, 2020.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Reinforcement learning upside down: Don't predict rewards -- just map them to actions, 2020

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.692774Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0ad97e9-02cb-4edd-8211-cb21fa4dab4d · outbound

This paper cites Rapid exploration for open-world navigation with latent goal models.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Rapid exploration for open-world navigation with latent goal models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.681724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfc8bf9e-530b-400e-a12c-6bd0d5fc4e6f · outbound

This paper cites Score models for offline goal-conditioned reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Score models for offline goal-conditioned reinforcement learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.670794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9e70b2b-72cf-44f6-996f-cfd0632dad84 · outbound

This paper cites Geoadditive expectile regression.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Geoadditive expectile regression

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.659954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9c00165-7d4f-4aeb-9178-49005ea0b73e · outbound

This paper cites Learning structured output representation using deep conditional generative models.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Learning structured output representation using deep conditional generative models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.014377Z

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Observation 31cf407e-cc0a-4685-ae51-73d4d982f234 · outbound

This paper cites Gymnasium (mar 2023), 2023.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Gymnasium (mar 2023), 2023

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.639864Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 15c15ca4-5ecf-46a0-abb1-078650721ce1 · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Deep Reinforcement Learning and the Deadly Triad

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.022196Z

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Observation 7e47b5a1-f2c6-4aec-b59c-e20c712aadcc · outbound

This paper cites Attention is all you need.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Attention is all you need

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.026210Z

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Observation c20b3ea7-d6cd-4f80-a058-c02026c8f25b · outbound

This paper cites GOP lan: Goal-conditioned offline reinforcement learning by planning with learned models.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization GOP lan: Goal-conditioned offline reinforcement learning by planning with learned models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.619349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34fa6ff0-f0f9-4b0f-bac5-f5c5f9efce2b · outbound

This paper cites Optimal goal-reaching reinforcement learning via quasimetric learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Optimal goal-reaching reinforcement learning via quasimetric learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.607776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4064caf9-0460-452a-a414-d3ea921924ee · outbound

This paper cites Critic-guided decision transformer for offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Critic-guided decision transformer for offline reinforcement learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.596625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f240bd1-3f82-44a7-900f-bb0dcb6eb6ec · outbound

This paper cites Supported policy optimization for offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Supported policy optimization for offline reinforcement learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.584880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8e42e3d-1cdb-42c8-9228-c14463d124a2 · outbound

This paper cites Elastic Decision Transformer.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Elastic Decision Transformer

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.045615Z

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Observation ca8c62b3-2b08-4809-9704-568102ecae9d · outbound

This paper cites Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.573852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 158ff2de-8365-47d0-809c-dfad1cc405e2 · outbound

This paper cites Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.054551Z

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Observation db0fa5c6-5581-4b25-87fb-013fcef04677 · outbound

This paper cites What is essential for unseen goal generalization of offline goal-conditioned rl? In International Conference on Machine Learning, pages 39543--39571.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization What is essential for unseen goal generalization of offline goal-conditioned rl? In International Conference on Machine Learning, pages 39543--39571

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.561418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77db9b91-9fca-46a6-9296-56fca0933486 · outbound

This paper cites Swapped goal-conditioned offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Swapped goal-conditioned offline reinforcement learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.062222Z

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Observation 2f8dd9a5-e9c1-4d76-af4a-b181000ab2fb · outbound

This paper cites Breadth-first exploration on adaptive grid for reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Breadth-first exploration on adaptive grid for reinforcement learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.066117Z

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Observation 7fbcd7cd-bb20-4774-be4e-c2841fc8cbee · outbound

This paper cites Goal-conditioned predictive coding for offline reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Goal-conditioned predictive coding for offline reinforcement learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.539901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b155832e-295f-432b-bfcd-1de1323f54b8 · outbound

This paper cites Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.072975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b355547-cf41-4fd4-860b-354833c8961b · outbound

This paper cites Contrastive difference predictive coding.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Contrastive difference predictive coding

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.526281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1007d93e-a759-4670-a9ae-ce0f34545b99 · outbound

This paper cites Online decision transformer.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Online decision transformer

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.514001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2de92e6a-ec70-4159-9a24-cf51af5f8e47 · outbound

This paper cites Behavior Proximal Policy Optimization.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Behavior Proximal Policy Optimization

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.085023Z

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Observation 1267e842-1074-4441-b705-b7277e3483cd · outbound

This paper cites Reinformer: Max-Return Sequence Modeling for Offline RL.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Reinformer: Max-Return Sequence Modeling for Offline RL

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.091332Z

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Observation ad6054d5-8274-4dd4-b327-6f158fb0db69 · outbound

This paper cites Revisiting the design choices in max-return sequence modeling, 2025.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Revisiting the design choices in max-return sequence modeling, 2025

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:05:26.500024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc578017-cce3-4fd5-bb09-46cadeecab2d · outbound

This paper cites Maximum entropy inverse reinforcement learning.

Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization Maximum entropy inverse reinforcement learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:26.101533Z

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