2,656 research outputs found
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and
decades of research on the development of conversational bots, building
automated agents that can carry on rich open-ended conversations with humans
"in the wild" remains a formidable challenge. In this work we develop a
real-time, open-ended dialogue system that uses reinforcement learning (RL) to
power a bot's conversational skill at scale. Our work pairs the succinct
embedding of the conversation state generated using SOTA (supervised) language
models with RL techniques that are particularly suited to a dynamic action
space that changes as the conversation progresses. Trained using crowd-sourced
data, our novel system is able to substantially exceeds the (strong) baseline
supervised model with respect to several metrics of interest in a live
experiment with real users of the Google Assistant
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
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