S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution. It has been widely successful in solving long-horizon and complex tasks, where flat-RL algorithms have difficulty in learning. However, while the low-level agent in HRL benefits from dense feedback and abundant trial opportunities, the high-level agent receives sparse, delayed feedback from the environment and its performance depends on the low-level execution capability. In this paper, we study whether subgoal selection by the high-level agent can be performed more strategically, by prov
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “S3: Stable Subgoal Selection by Constraining Uncertainty of ” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
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“Fuzzy title match (0.73): “S3: Stable Subgoal Selection by Constraining Uncertainty of ” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
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- PossiblePossibly related (embedding) · 51%Gradient-based Planning for World Models at Longer Horizons →
- FuzzySimilar title/name (fuzzy) · 59%DLR-RM/stable-baselines3 →
“Fuzzy title match (0.73): “S3: Stable Subgoal Selection by Constraining Uncertainty of ” ≈ “DLR-RM/stable-baselines3””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
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- LinkedLinked via arxiv author · 85%Kshitij Kumar Srivastava →
“S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Kshitij Jerath →
“S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning”