334,935 research outputs found
AI2-THOR: An Interactive 3D Environment for Visual AI
We introduce The House Of inteRactions (THOR), a framework for visual AI
research, available at http://ai2thor.allenai.org. AI2-THOR consists of near
photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes
and interact with objects to perform tasks. AI2-THOR enables research in many
different domains including but not limited to deep reinforcement learning,
imitation learning, learning by interaction, planning, visual question
answering, unsupervised representation learning, object detection and
segmentation, and learning models of cognition. The goal of AI2-THOR is to
facilitate building visually intelligent models and push the research forward
in this domain
Emergence of Grounded Compositional Language in Multi-Agent Populations
By capturing statistical patterns in large corpora, machine learning has
enabled significant advances in natural language processing, including in
machine translation, question answering, and sentiment analysis. However, for
agents to intelligently interact with humans, simply capturing the statistical
patterns is insufficient. In this paper we investigate if, and how, grounded
compositional language can emerge as a means to achieve goals in multi-agent
populations. Towards this end, we propose a multi-agent learning environment
and learning methods that bring about emergence of a basic compositional
language. This language is represented as streams of abstract discrete symbols
uttered by agents over time, but nonetheless has a coherent structure that
possesses a defined vocabulary and syntax. We also observe emergence of
non-verbal communication such as pointing and guiding when language
communication is unavailable
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to
accomplish a given task is a core area of research in artificial intelligence
and machine learning. Towards this goal, the Autonomous Agents Research Group
develops novel machine learning algorithms for autonomous systems control, with
a specific focus on deep reinforcement learning and multi-agent reinforcement
learning. Research problems include scalable learning of coordinated agent
policies and inter-agent communication; reasoning about the behaviours, goals,
and composition of other agents from limited observations; and sample-efficient
learning based on intrinsic motivation, curriculum learning, causal inference,
and representation learning. This article provides a broad overview of the
ongoing research portfolio of the group and discusses open problems for future
directions.Comment: Published in AI Communications Special Issue on Multi-Agent Systems
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