Hierarchical Reinforcement Learning
87 papers with code • 0 benchmarks • 2 datasets
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Gibson Environment
bipedal-skills
Latest papers with no code
SLIM: Skill Learning with Multiple Critics
As it requires impacting a possibly large set of degrees of freedom composing the environment, mutual information maximization fails alone in producing useful and safe manipulation behaviors.
Reconciling Spatial and Temporal Abstractions for Goal Representation
In this paper, we propose a novel three-layer HRL algorithm that introduces, at different levels of the hierarchy, both a spatial and a temporal goal abstraction.
Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation
In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences.
Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Based on hierarchical reinforcement learning, MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session bidding for AFL MUs, with the objective of maximizing the total utility.
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments
Modern HRL typically designs a hierarchical agent composed of a high-level policy and low-level policies.
A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
Designing controllers to achieve natural motor capabilities for multi-joint robots is a significant challenge.
Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the transformer architecture in reinforcement learning (RL).
MTAC: Hierarchical Reinforcement Learning-based Multi-gait Terrain-adaptive Quadruped Controller
Urban search and rescue missions require rapid first response to minimize loss of life and damage.
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
We solve this problem by learning a sequence of actions that utilize the environment to change the object's pose.
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and thus enables sample-efficient learning.
