Hierarchical Reinforcement Learning

54 papers with code • 0 benchmarks • 1 datasets

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Greatest papers with code

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.

Hierarchical Reinforcement Learning

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

maximecb/gym-minigrid 1 Jul 2019

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

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

maximecb/gym-minigrid ICLR 2020

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.

Hierarchical Reinforcement Learning

Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System

budzianowski/multiwoz 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.

Hierarchical Reinforcement Learning Hierarchical structure +1

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.

Entity Extraction using GAN Hierarchical Reinforcement Learning +1

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.

Decision Making Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning for Open-Domain Dialog

natashamjaques/neural_chat 17 Sep 2019

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 Open-Domain Dialog

Compositional Generalization by Learning Analytical Expressions

microsoft/ContextualSP NeurIPS 2020

Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.

Hierarchical Reinforcement Learning