mlsanityData to 14 Oct 2026

Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph

arXiv 2103.01350 ↗First posted 1 Mar 2021cs.CV cs.ROcited by 7

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Abstract (arXiv)

We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical process that facilitates: 1) the high-level network raising a sub-goal towards achieving a designated final goal; 2) the low-level network towards an optimal policy; and 3) the overall system generalizing unseen environments and goals. We evaluate our approach with two settings of partially observable goal-driven tasks -- a grid-world domain and a robotic object search task. Our experimental results show that our approach exhibits superior generalization performance on both unseen environments and new goals.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 34 of its 40 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

34 of 40 references.

References · 34

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2015Continuous control with deep reinforcement learningcited by 3,407
  5. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  6. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,656
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  8. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  9. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,027
  10. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
  11. 2018On Evaluation of Embodied Navigation Agentscited by 547
  12. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
22 more
  1. 2016The Option-Critic Architecturecited by 448
  2. 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 429
  3. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
  4. 2018Speaker-Follower Models for Vision-and-Language Navigationcited by 340
  5. 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
  6. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 286
  7. 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 244
  8. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 231
  9. 2017Cognitive Mapping and Planning for Visual Navigationcited by 212
  10. 2018Semi-parametric Topological Memory for Navigationcited by 209
  11. 2019Planning with Goal-Conditioned Policiescited by 149
  12. 2018Visual Semantic Navigation using Scene Priorscited by 128
  13. 2018Deep Variational Reinforcement Learning for POMDPscited by 125
  14. 2018Building Generalizable Agents with a Realistic and Rich 3D Environmentcited by 109
  15. 2018Visual Representations for Semantic Target Driven Navigationcited by 89
  16. 2018Neural Modular Control for Embodied Question Answeringcited by 56
  17. 2019Bayesian Relational Memory for Semantic Visual Navigationcited by 48
  18. 2019Variational Recurrent Models for Solving Partially Observable Control Taskscited by 47
  19. 2020Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modelingcited by 9
  20. 2018Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robotscited by 7
  21. 2018GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environmentcited by 3
  22. 2020Learning hierarchical relationships for object-goal navigationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2024TopV-Nav: Unlocking the Top-View Spatial Reasoning Potential of MLLM for Zero-shot Object Navigationcited by 15
  2. 2023Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environmentscited by 9
  3. 2022Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigationcited by 6
  4. 2023Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidancecited by 3
  5. 2024Diffusion as Reasoning: Enhancing Object Navigation via Diffusion Model Conditioned on LLM-based Object-Room Knowledgecited by 1
  6. 2021Dialogue Object Searchcited by 0
  7. 2026Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigationcited by 0