Search Results for author: Heriberto Cuayáhuitl

Found 10 papers, 4 papers with code

A Survey on Deep Reinforcement Learning for Audio-Based Applications

no code implementations1 Jan 2021 Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria

We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.

Audio Signal Processing reinforcement-learning +1

Ensemble-Based Deep Reinforcement Learning for Chatbots

no code implementations27 Aug 2019 Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Yongjin Cho, Sungja Choi, Satish Indurthi, Seunghak Yu, Hyungtak Choi, Inchul Hwang, Jihie Kim

Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.

Chatbot reinforcement-learning +1

Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards

no code implementations27 Aug 2019 Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Sungja Choi, Inchul Hwang, Jihie Kim

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function.

reinforcement-learning reinforcement Learning +2

A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots

no code implementations27 Aug 2019 Heriberto Cuayáhuitl

The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously.

Robot Manipulation

A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents

no code implementations2 Dec 2018 Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee, Jihie Kim

The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way.

Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning

1 code implementation26 Nov 2016 Heriberto Cuayáhuitl, Guillaume Couly, Clément Olalainty

Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions.

reinforcement-learning reinforcement Learning

Deep Reinforcement Learning for Multi-Domain Dialogue Systems

1 code implementation26 Nov 2016 Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, Jacob Carse

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems.

reinforcement-learning reinforcement Learning

SimpleDS: A Simple Deep Reinforcement Learning Dialogue System

1 code implementation18 Jan 2016 Heriberto Cuayáhuitl

This paper presents 'SimpleDS', a simple and publicly available dialogue system trained with deep reinforcement learning.

Feature Engineering reinforcement-learning +1

Strategic Dialogue Management via Deep Reinforcement Learning

1 code implementation25 Nov 2015 Heriberto Cuayáhuitl, Simon Keizer, Oliver Lemon

This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting.

Dialogue Management Management +2

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