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Found 10 papers, 4 papers with code
Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations
We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset.
A Survey on Deep Reinforcement Learning for Audio-Based Applications
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.
Ensemble-Based Deep Reinforcement Learning for Chatbots
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.
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
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.
A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots
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.
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
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
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.
Deep Reinforcement Learning for Multi-Domain Dialogue Systems
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems.
SimpleDS: A Simple Deep Reinforcement Learning Dialogue System
This paper presents 'SimpleDS', a simple and publicly available dialogue system trained with deep reinforcement learning.
Strategic Dialogue Management via Deep Reinforcement Learning
This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting.
