Browse > Methodology > Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning

17 papers with code · Methodology

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

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

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

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

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

22 Nov 2018tesatory/hsp

In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies.

HIERARCHICAL REINFORCEMENT LEARNING

Safe Option-Critic: Learning Safety in the Option-Critic Architecture

21 Jul 2018arushi12130/SafeOptionCritic

We then derive the policy-gradient theorem with controllability and propose a novel framework called safe option-critic.

ATARI GAMES HIERARCHICAL REINFORCEMENT LEARNING

Keep it stupid simple

10 Sep 2018CoAxLab/azad

Deep reinforcement learning can match and exceed human performance, but if even minor changes are introduced to the environment artificial networks often can't adapt.

HIERARCHICAL REINFORCEMENT LEARNING