mlsanityData to 14 Oct 2026

How to Solve Contextual Goal-Oriented Problems with Offline Datasets?

arXiv 2408.07753 ↗First posted 14 Aug 2024cs.LGcited by 1

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Abstract (arXiv)

We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems in the offline data setup. Empirical results also showcase the effectiveness of CODA, which outperforms other baseline methods across various context-goal relationships of CGO problem. This approach offers a promising direction to solving CGO problems using offline datasets.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 45 references that are in the map. First posted on arXiv 14 Aug 2024.

Builds on

39 of 45 references.

References · 39

  1. 2022Classifier-Free Diffusion Guidancecited by 5,847
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
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  8. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
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  10. 2019Behavior Regularized Offline Reinforcement Learningcited by 662
  11. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 657
  12. 2020MOPO: Model-based Offline Policy Optimizationcited by 636
27 more
  1. 2020Is Pessimism Provably Efficient for Offline RL?cited by 364
  2. 2016Successor Features for Transfer in Reinforcement Learningcited by 340
  3. 2019Learning Latent Plans from Playcited by 316
  4. 2021Bellman-consistent Pessimism for Offline Reinforcement Learningcited by 269
  5. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  6. 2018Visual Reinforcement Learning with Imagined Goalscited by 251
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  8. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 229
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  11. 2018Multi-task Deep Reinforcement Learning with PopArtcited by 141
  12. 2021Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skillscited by 125
  13. 2022Adversarially Trained Actor Critic for Offline Reinforcement Learningcited by 111
  14. 2018Learning to Navigate in Cities Without a Mapcited by 104
  15. 2019Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generationcited by 83
  16. 2020COG: Connecting New Skills to Past Experience with Offline Reinforcement Learningcited by 83
  17. 2022How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage Regressioncited by 66
  18. 2021No RL, No Simulation: Learning to Navigate without Navigatingcited by 65
  19. 2021Conservative Data Sharing for Multi-Task Offline Reinforcement Learningcited by 64
  20. 2022How to Leverage Unlabeled Data in Offline Reinforcement Learningcited by 62
  21. 2020Generalized Hindsight for Reinforcement Learningcited by 51
  22. 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 44
  23. 2019Contextual Imagined Goals for Self-Supervised Robotic Learningcited by 40
  24. 2023What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?cited by 36
  25. 2023MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observationscited by 18
  26. 2022Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Taskscited by 17
  27. 2021Learning Domain Invariant Representations in Goal-conditioned Block MDPscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Actioncited by 4