Hierarchical Reinforcement Learning
87 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find Hierarchical Reinforcement Learning models and implementations
Gibson Environment
bipedal-skills
Most implemented papers
Learning Fairness in Multi-Agent Systems
PKU-AI-Edge/FEN
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NeurIPS 2019
Fairness is essential for human society, contributing to stability and productivity.
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
qiang-ma/graph-pointer-network
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Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
mikezhang95/HDNO
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ICLR 2021
We test HDNO on MultiWoz 2. 0 and MultiWoz 2. 1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing improvements on the performance evaluated by automatic evaluation metrics and human evaluation.
Online Baum-Welch algorithm for Hierarchical Imitation Learning
VittorioGiammarino/Online_BWforHIL
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This problem is referred to as hierarchical imitation learning and can be handled as an inference problem in a Hidden Markov Model, which is done via an Expectation-Maximization type algorithm.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
A Framework for Constrained and Adaptive Behavior-Based Agents
Behavior Trees are commonly used to model agents for robotics and games, where constrained behaviors must be designed by human experts in order to guarantee that these agents will execute a specific chain of actions given a specific set of perceptions.
FeUdal Networks for Hierarchical Reinforcement Learning
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning.
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
lyebi/Test
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We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key.
Crossmodal Attentive Skill Learner
shayegano/CASL
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This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs.
Logically-Constrained Reinforcement Learning
With this reward function, the policy synthesis procedure is "constrained" by the given specification.
