9 research outputs found
Grounding Language for Transfer in Deep Reinforcement Learning
In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.Comment: JAIR 201
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural
speech. As such, they have many benefits. By using speech as the primary
communication medium, a computer interface can facilitate swift, human-like
acquisition of information. In recent years, speech interfaces have become ever
more popular, as is evident from the rise of personal assistants such as Siri,
Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning
methods have been applied to dialogue modelling and the results achieved for
limited-domain applications are comparable to or outperform traditional
approaches. Methods based on Gaussian processes are particularly effective as
they enable good models to be estimated from limited training data.
Furthermore, they provide an explicit estimate of the uncertainty which is
particularly useful for reinforcement learning. This article explores the
additional steps that are necessary to extend these methods to model multiple
dialogue domains. We show that Gaussian process reinforcement learning is an
elegant framework that naturally supports a range of methods, including prior
knowledge, Bayesian committee machines and multi-agent learning, for
facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
Language Learning in Interactive Environments
Natural language communication has long been considered a defining characteristic of human intelligence. I am motivated by the question of how learning agents can understand and generate contextually relevant natural language in service of achieving a goal. In pursuit of this objective, I have been studying Interactive Narratives, or text-adventures: simulations in which an agent interacts with the world purely through natural language—"seeing” and “acting upon” the world using textual descriptions and commands. These games are usually structured as puzzles or quests in which a player must complete a sequence of actions to succeed. My work studies two closely related aspects of Interactive Narratives: operating in these environments and creating them in addition to their intersection—each presenting its own set of unique challenges.
Operating in these environments presents three challenges: (1) Knowledge representation—an agent must maintain a persistent memory of what it has learned through its experiences with a partially observable world; (2) Commonsense reasoning to endow the agent with priors on how to interact with the world around it; and (3) Scaling to effectively explore sparse-reward, combinatorially-sized natural language state-action spaces. On the other hand, creating these environments can be split into two complementary considerations: (1) World generation, or the problem of creating a world that defines the limits of the actions an agent can perform; and (2) Quest generation, i.e. defining actionable objectives grounded in a given world. I will present my work thus far—showcasing how structured, interpretable data representations in the form of knowledge graphs aid in each of these tasks—in addition to proposing how exactly these two aspects of Interactive Narratives can be combined to improve language learning and generalization across this board of challenges.Ph.D