In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

When learning policies for real-world domains, two important questions arise:(i) how to
efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate …

Much of human dialogue occurs in semi-cooperative settings, where agents with different
goals attempt to agree on common decisions. Negotiations require complex communication …

During the past decade, several areas of speech and language understanding have
witnessed substantial breakthroughs from the use of data-driven models. In the area of …

Offline methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …

Widely-used deep reinforcement learning algorithms have been shown to fail in the batch
setting--learning from a fixed data set without interaction with the environment. Following this …

This paper presents the Frames dataset (Frames is available at http://datasets. maluuba.
com/Frames), a corpus of 1369 human-human dialogues with an average of 15 turns per …

Conventionally, generation of natural language for dialogue agents may be viewed as a
statistical learning problem: determine the patterns in human-provided data and generate …