Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Neary, Cyrus"'
We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware. By decomposin
Externí odkaz:
http://arxiv.org/abs/2312.01249
Autor:
Wongpiromsarn, Tichakorn, Ghasemi, Mahsa, Cubuktepe, Murat, Bakirtzis, Georgios, Carr, Steven, Karabag, Mustafa O., Neary, Cyrus, Gohari, Parham, Topcu, Ufuk
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main building blocks of formal methods are models and specifications, which are
Externí odkaz:
http://arxiv.org/abs/2311.01258
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task. The framework consists of a hi
Externí odkaz:
http://arxiv.org/abs/2309.06420
Recently developed pretrained models can encode rich world knowledge expressed in multiple modalities, such as text and images. However, the outputs of these models cannot be integrated into algorithms to solve sequential decision-making tasks. We de
Externí odkaz:
http://arxiv.org/abs/2308.05295
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to leverage
Externí odkaz:
http://arxiv.org/abs/2306.06335
Privacy-aware multiagent systems must protect agents' sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a framework to privatize inter-agent communications in cooperative multia
Externí odkaz:
http://arxiv.org/abs/2301.08811
Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often require excessive sample sizes, which may be infeasible in real-world sce
Externí odkaz:
http://arxiv.org/abs/2301.03565
Automaton-based representations of task knowledge play an important role in control and planning for sequential decision-making problems. However, obtaining the high-level task knowledge required to build such automata is often difficult. Meanwhile,
Externí odkaz:
http://arxiv.org/abs/2212.01944
Autor:
Neary, Cyrus, Topcu, Ufuk
Many dynamical systems -- from robots interacting with their surroundings to large-scale multiphysics systems -- involve a number of interacting subsystems. Toward the objective of learning composite models of such systems from data, we present i) a
Externí odkaz:
http://arxiv.org/abs/2212.00893
In a cooperative multiagent system, a collection of agents executes a joint policy in order to achieve some common objective. The successful deployment of such systems hinges on the availability of reliable inter-agent communication. However, many so
Externí odkaz:
http://arxiv.org/abs/2201.06619