OGBench: Benchmarking Offline Goal-Conditioned RL
Fuente:
arXiv
Saved in:
| Main Authors: | Park, Seohong, Frans, Kevin, Eysenbach, Benjamin, Levine, Sergey |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
by: Park, Seohong, et al.
Published: (2023)
by: Park, Seohong, et al.
Published: (2023)
Is Value Learning Really the Main Bottleneck in Offline RL?
by: Park, Seohong, et al.
Published: (2024)
by: Park, Seohong, et al.
Published: (2024)
Horizon Reduction Makes RL Scalable
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Intention-Conditioned Flow Occupancy Models
by: Zheng, Chongyi, et al.
Published: (2025)
by: Zheng, Chongyi, et al.
Published: (2025)
Scalable Offline Model-Based RL with Action Chunks
by: Park, Kwanyoung, et al.
Published: (2025)
by: Park, Kwanyoung, et al.
Published: (2025)
Dual Goal Representations
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
by: Frans, Kevin, et al.
Published: (2024)
by: Frans, Kevin, et al.
Published: (2024)
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
by: Zheng, Chongyi, et al.
Published: (2023)
by: Zheng, Chongyi, et al.
Published: (2023)
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
by: Park, Seohong, et al.
Published: (2023)
by: Park, Seohong, et al.
Published: (2023)
Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Flow Q-Learning
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Accelerating Goal-Conditioned RL Algorithms and Research
by: Bortkiewicz, Michał, et al.
Published: (2024)
by: Bortkiewicz, Michał, et al.
Published: (2024)
Foundation Policies with Hilbert Representations
by: Park, Seohong, et al.
Published: (2024)
by: Park, Seohong, et al.
Published: (2024)
Decoupled Q-Chunking
by: Li, Qiyang, et al.
Published: (2025)
by: Li, Qiyang, et al.
Published: (2025)
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
by: Modirshanechi, Alireza, et al.
Published: (2026)
by: Modirshanechi, Alireza, et al.
Published: (2026)
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
by: Wang, Kevin, et al.
Published: (2025)
by: Wang, Kevin, et al.
Published: (2025)
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals
by: Liu, Grace, et al.
Published: (2024)
by: Liu, Grace, et al.
Published: (2024)
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
by: Choi, Jinwoo, et al.
Published: (2026)
by: Choi, Jinwoo, et al.
Published: (2026)
Diffusion Guidance Is a Controllable Policy Improvement Operator
by: Frans, Kevin, et al.
Published: (2025)
by: Frans, Kevin, et al.
Published: (2025)
Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations
by: Myers, Vivek, et al.
Published: (2025)
by: Myers, Vivek, et al.
Published: (2025)
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
by: Myers, Vivek, et al.
Published: (2024)
by: Myers, Vivek, et al.
Published: (2024)
Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration
by: Wilcoxson, Max, et al.
Published: (2024)
by: Wilcoxson, Max, et al.
Published: (2024)
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
by: Bortkiewicz, Michał, et al.
Published: (2025)
by: Bortkiewicz, Michał, et al.
Published: (2025)
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
by: Nimonkar, Chirayu, et al.
Published: (2025)
by: Nimonkar, Chirayu, et al.
Published: (2025)
Training LLM Agents to Empower Humans
by: Ellis, Evan, et al.
Published: (2025)
by: Ellis, Evan, et al.
Published: (2025)
Planning without Search: Refining Frontier LLMs with Offline Goal-Conditioned RL
by: Hong, Joey, et al.
Published: (2025)
by: Hong, Joey, et al.
Published: (2025)
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
by: Bae, Junik, et al.
Published: (2024)
by: Bae, Junik, et al.
Published: (2024)
Abstraction for Offline Goal-Conditioned Reinforcement Learning
by: Wibault, Clarisse, et al.
Published: (2026)
by: Wibault, Clarisse, et al.
Published: (2026)
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images
by: Hatch, Kyle B., et al.
Published: (2024)
by: Hatch, Kyle B., et al.
Published: (2024)
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
by: Nakamoto, Mitsuhiko, et al.
Published: (2023)
by: Nakamoto, Mitsuhiko, et al.
Published: (2023)
RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
by: Stachowicz, Kyle, et al.
Published: (2024)
by: Stachowicz, Kyle, et al.
Published: (2024)
Learning to Assist Humans without Inferring Rewards
by: Myers, Vivek, et al.
Published: (2024)
by: Myers, Vivek, et al.
Published: (2024)
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
by: Huang, Xingshuai, et al.
Published: (2024)
by: Huang, Xingshuai, et al.
Published: (2024)
Language-Conditioned Offline RL for Multi-Robot Navigation
by: Morad, Steven, et al.
Published: (2024)
by: Morad, Steven, et al.
Published: (2024)
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data
by: Dashora, Nitish, et al.
Published: (2025)
by: Dashora, Nitish, et al.
Published: (2025)
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
by: Sikchi, Harshit, et al.
Published: (2023)
by: Sikchi, Harshit, et al.
Published: (2023)
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
by: Kuba, Jakub Grudzien, et al.
Published: (2024)
by: Kuba, Jakub Grudzien, et al.
Published: (2024)
Bridging State and History Representations: Understanding Self-Predictive RL
by: Ni, Tianwei, et al.
Published: (2024)
by: Ni, Tianwei, et al.
Published: (2024)
Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
by: Ahn, Hongjoon, et al.
Published: (2025)
by: Ahn, Hongjoon, et al.
Published: (2025)
Unsupervised-to-Online Reinforcement Learning
by: Kim, Junsu, et al.
Published: (2024)
by: Kim, Junsu, et al.
Published: (2024)
Similar Items
-
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
by: Park, Seohong, et al.
Published: (2023) -
Is Value Learning Really the Main Bottleneck in Offline RL?
by: Park, Seohong, et al.
Published: (2024) -
Horizon Reduction Makes RL Scalable
by: Park, Seohong, et al.
Published: (2025) -
Intention-Conditioned Flow Occupancy Models
by: Zheng, Chongyi, et al.
Published: (2025) -
Scalable Offline Model-Based RL with Action Chunks
by: Park, Kwanyoung, et al.
Published: (2025)