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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

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

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

ICLR 2020 maximecb/gym-minigrid

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

ICLR 2021 budzianowski/multiwoz

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 TASK-ORIENTED DIALOGUE SYSTEMS

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 USING GAN HIERARCHICAL REINFORCEMENT LEARNING RELATION EXTRACTION

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 OPEN-DOMAIN DIALOG

Inter-Level Cooperation in Hierarchical Reinforcement Learning

5 Dec 2019AboudyKreidieh/h-baselines

In this paper, we address instabilities that arise from the concurrent optimization of goal-assignment and goal-achievement policies.

HIERARCHICAL REINFORCEMENT LEARNING