Hierarchical Reinforcement Learning
88 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Hierarchical Reinforcement Learning
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Libraries
Use these libraries to find Hierarchical Reinforcement Learning models and implementations
Gibson Environment
bipedal-skills
Latest papers with no code
Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption
This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO).
Hierarchical reinforcement learning with natural language subgoals
Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
Spread Control Method on Unknown Networks Based on Hierarchical Reinforcement Learning
Epidemics such as COVID-19 pose serious threats to public health and our society, and it is critical to investigate effective methods to control the spread of epidemics over networks.
Joint Band Assignment and Beam Management using Hierarchical Reinforcement Learning for Multi-Band Communication
In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management.
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers.
Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning
However, with conventional RL methods, the decision variables (though being deployed at various network layers) are typically optimized in the same control loop, leading to significant practical limitations on the control loop's delay as well as excessive signaling and energy consumption.
Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach
The integrated development of city clusters has given rise to an increasing demand for intercity travel.
Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
We present HIMOS, a hierarchical reinforcement learning approach that learns to compose exploration, navigation, and manipulation skills.
