Hierarchical Reinforcement Learning

79 papers with code • 0 benchmarks • 2 datasets

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Use these libraries to find Hierarchical Reinforcement Learning models and implementations
3 papers

Most implemented papers

Data-Efficient Hierarchical Reinforcement Learning

tensorflow/models NeurIPS 2018

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.

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

tensorflow/models ICLR 2019

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

borea17/efficient_rl 21 May 1999

The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction.

Learning Multi-Level Hierarchies with Hindsight

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

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.

Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning

nnbay/MeicalChatbot-HRL 29 Apr 2020

In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting.

Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

CC1st/-step-by-step-mindspore Knowledge-Based Systems 2022

Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.

Stochastic Neural Networks for Hierarchical Reinforcement Learning

florensacc/snn4hrl 10 Apr 2017

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.

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

truthless11/HRL-RE 9 Nov 2018

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.

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

tesatory/hsp 22 Nov 2018

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