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Hierarchical Reinforcement Learning

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Ant + Maze

Libraries

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grockious/lcrl

3 papers 52

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Gibson Environment

bipedal-skills

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Most implemented papers

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

Nando de Freitas, E. Brochu, Vlad M. Cora•Sat Dec 11 2010

A tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions using the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function.

2590

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Data-Efficient Hierarchical Reinforcement Learning

S. Levine, Honglak Lee, Ofir Nachum, S. Gu•Sun May 20 2018

This paper studies how to 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.

928 0

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

S. Levine, Honglak Lee, Ofir Nachum, S. Gu•Mon Oct 01 2018

Results on a number of difficult continuous-control tasks show that the developed notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation, yields qualitatively better representations as well as quantitatively better hierarchical policies compared to existing methods.

225 0

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Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

Thomas G. Dietterich•Thu May 20 1999

The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges with 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.

1812 0

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Hierarchical reinforcement learning for automatic disease diagnosis

Xuanjing Huang, Baolin Peng, Zhongyu Wei, Cheng Zhong, Kangenbei Liao, W. Chen, Qianlong Liu, J. Peng•Thu Jun 30 2022

Inspired by the offline consultation process, this work proposes to integrate a hierarchical policy structure of two levels into the dialogue system for policy learning that achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems.

28 0

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Stochastic Neural Networks for Hierarchical Reinforcement Learning

P. Abbeel, Yan Duan, Carlos Florensa•Fri Nov 04 2016

This work proposes a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks, and uses Stochastic Neural Networks combined with an information-theoretic regularizer to efficiently pre-train a large span of skills.

374 0

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A Hierarchical Framework for Relation Extraction with Reinforcement Learning

Minlie Huang, Tianyang Zhang, Ryuichi Takanobu, Jiexi Liu•Thu Nov 08 2018

This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation and applies a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types.

241 0

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Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

Emily L. Denton, Arthur Szlam, R. Fergus, Sainbayar Sukhbaatar•Wed Nov 21 2018

This work proposes an unsupervised learning scheme, based on asymmetric self-play from Sukhbaatar et al. (2018), that automatically learns a good representation of sub-goals in the environment and a low-level policy that can execute them.

46 0

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Hierarchical Reinforcement Learning | State-of-the-Art