5 papers
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signal…
Ensemble-Based Deep Reinforcement Learning for Chatbots
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +7
Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing…
A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots
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 autono…
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +3
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward functi…
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee +1
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. This suggests that principled in…