537 research outputs found
Design of of model-based controllers via parametric programming
Imperial Users onl
Applications of linear estimation theory to chemical processes
Imperial Users onl
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
Reparameterized Policy Learning for Multimodal Trajectory Optimization
We investigate the challenge of parametrizing policies for reinforcement
learning (RL) in high-dimensional continuous action spaces. Our objective is to
develop a multimodal policy that overcomes limitations inherent in the
commonly-used Gaussian parameterization. To achieve this, we propose a
principled framework that models the continuous RL policy as a generative model
of optimal trajectories. By conditioning the policy on a latent variable, we
derive a novel variational bound as the optimization objective, which promotes
exploration of the environment. We then present a practical model-based RL
method, called Reparameterized Policy Gradient (RPG), which leverages the
multimodal policy parameterization and learned world model to achieve strong
exploration capabilities and high data efficiency. Empirical results
demonstrate that our method can help agents evade local optima in tasks with
dense rewards and solve challenging sparse-reward environments by incorporating
an object-centric intrinsic reward. Our method consistently outperforms
previous approaches across a range of tasks. Code and supplementary materials
are available on the project page https://haosulab.github.io/RPG
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