Глубокое обучение с подкреплением

method ID: deepreinforcementlearning

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Language Understanding for Text-based Games using Deep Reinforcement Learning

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

In this paper, we consider the task of learn- ing control policies for text-based games. In these games, all interactions in the vir- tual world are through text and the …...

34938

Deep Reinforcement Learning for Dialogue Generation

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be short- sighted, predicting utterances one at a time while ignoring their …...

33597

Sentence Simplification with Deep Reinforcement Learning

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractSentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and …...

33059

A New Concept of Deep Reinforcement Learning based Augmented General Tagging System

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractIn this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model …...

5373

Mining Evidences for Concept Stock Recommendation

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Publication: TIPSTER (2018)

DOI: nan

AbstractWe investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to …...

20847

Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractDeep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward Rouge-L simply …...

8906

Learning Dialog Policies from Weak Demonstrations

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractDeep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems. Building upon Deep …...

38178

Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractDeep reinforcement learning has shown great potential in training dialogue policies. However, its favorable performance comes at the cost of many rounds of interaction. Most of the existing dialogue policy …...

31750

High-Quality Dialogue Diversification by Intermittent Short Extension Ensembles

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Publication: TIPSTER (2021)

DOI: nan

Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and com- plete the tasks successfully. Training DRL agents with diverse dialogue …...

34019

Neuro-Symbolic Reinforcement Learning with First-Order Logic

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractDeep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the …...

31820

AVAST: Attentive Variational State Tracker in a Reinforced Navigator

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Publication: TIPSTER (2022)

DOI: nan

AbstractRecently, emerging approaches have been proposed to deal with robotic navigation problems, especially vision-and-language navigation task which is one of the most realistic indoor navigation challenge tasks. This task can …...

572

Interactive Query-Assisted Summarization via Deep Reinforcement Learning

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Publication: TIPSTER (2022)

DOI: nan

AbstractInteractive summarization is a task that facilitates user-guided exploration of information within a document set. While one would like to employ state of the art neural models to improve the …...

21224

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractText-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games. On the other hand, neuro-symbolic …...

38489