49,306 research outputs found
Planning with Sequence Models through Iterative Energy Minimization
Recent works have shown that sequence modeling can be effectively used to
train reinforcement learning (RL) policies. However, the success of applying
existing sequence models to planning, in which we wish to obtain a trajectory
of actions to reach some goal, is less straightforward. The typical
autoregressive generation procedures of sequence models preclude sequential
refinement of earlier steps, which limits the effectiveness of a predicted
plan. In this paper, we suggest an approach towards integrating planning with
sequence models based on the idea of iterative energy minimization, and
illustrate how such a procedure leads to improved RL performance across
different tasks. We train a masked language model to capture an implicit energy
function over trajectories of actions, and formulate planning as finding a
trajectory of actions with minimum energy. We illustrate how this procedure
enables improved performance over recent approaches across BabyAI and Atari
environments. We further demonstrate unique benefits of our iterative
optimization procedure, involving new task generalization, test-time
constraints adaptation, and the ability to compose plans together. Project
website: https://hychen-naza.github.io/projects/LEAPComment: Accepted by ICLR2023. Project page:
https://hychen-naza.github.io/projects/LEAP/index.htm
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Tri-N-ification
We consider a natural generalization of trinification to theories with 3N
SU(3) gauge groups. These theories have a simple moose representation and a
gauge boson spectrum that can be interpreted via the deconstruction of a 5D
theory with unified symmetry broken on a boundary. Although the matter and
Higgs sectors of the theory have no simple extra-dimensional analog, gauge
unification retains features characteristic of the 5D theory. We determine
possible assignments of the matter and Higgs fields to unified multiplets and
present theories that are viable alternatives to minimal trinified GUTs.Comment: 21 pages LaTeX, 6 eps figure
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