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Hierarchical Reinforcement Learning

21 papers with code · Methodology

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Data-Efficient Hierarchical Reinforcement Learning

NeurIPS 2018 tensorflow/models

In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.

HIERARCHICAL REINFORCEMENT LEARNING

Data-Efficient Hierarchical Reinforcement Learning

NeurIPS 2018 tensorflow/models

In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.

HIERARCHICAL REINFORCEMENT LEARNING

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

1 Jul 2019maximecb/gym-minigrid

We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.

HIERARCHICAL REINFORCEMENT LEARNING

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

9 Nov 2018truthless11/HRL-RE

The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.

ENTITY EXTRACTION HIERARCHICAL REINFORCEMENT LEARNING RELATION EXTRACTION

Stochastic Neural Networks for Hierarchical Reinforcement Learning

10 Apr 2017florensacc/snn4hrl

Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.

HIERARCHICAL REINFORCEMENT LEARNING

Learning Multi-Level Hierarchies with Hindsight

4 Dec 2017andrew-j-levy/Hierarchical-Actor-Critc-HAC-

Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions.

DECISION MAKING HIERARCHICAL REINFORCEMENT LEARNING

Hierarchical Reinforcement Learning for Open-Domain Dialog

17 Sep 2019natashamjaques/neural_chat

Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text.

HIERARCHICAL REINFORCEMENT LEARNING

Diversity-Driven Extensible Hierarchical Reinforcement Learning

10 Nov 2018YuhangSong/DEHRL

However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.

HIERARCHICAL REINFORCEMENT LEARNING