Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Dialogue system

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

Naacl 2019

Abstract

为对话定义action space并通过强化学习来优化其决策的过程是一个长期以来的挑战。通常的做法是使用手工来定义对话动作或者词表作为动作空间，两者都有各自的局限性。本文提出了一种潜在动作框架，将端到端agent的动作空间作为潜在变量，并应用非监督方法，从数据中归纳出自己的动作空间。

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A3C Actor-Critic Curiosity Curriculum Learning Data Augmentation Dialogue system Latent Variable Memory Network Multi-task Learning NLU Paper overview Reinforcement Learning Self-paced Learning User Simulator Visual Dialog jupyter meta-learning reward word2vec

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A3C 1
Actor-Critic 1
Curiosity 1
Curriculum Learning 3
Data Augmentation 1
Dialogue system 2
Latent Variable 1
Memory Network 1
Multi-task Learning 2
NLU 1
Paper overview 1
Reinforcement Learning 6
Self-paced Learning 1
User Simulator 1
Visual Dialog 1
jupyter 1
meta-learning 1
reward 1
word2vec 1

Tag Cloud

A3C Actor-Critic Curiosity Curriculum Learning Data Augmentation Dialogue system Latent Variable Memory Network Multi-task Learning NLU Paper overview Reinforcement Learning Self-paced Learning User Simulator Visual Dialog jupyter meta-learning reward word2vec

Recent

Archives

Tags

A3C 1
Actor-Critic 1
Curiosity 1
Curriculum Learning 3
Data Augmentation 1
Dialogue system 2
Latent Variable 1
Memory Network 1
Multi-task Learning 2
NLU 1
Paper overview 1
Reinforcement Learning 6
Self-paced Learning 1
User Simulator 1
Visual Dialog 1
jupyter 1
meta-learning 1
reward 1
word2vec 1
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