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pro vyhledávání: '"Khattar, Vanshaj"'
Autor:
Khattar, Vanshaj, Jin, Ming
Publikováno v:
American Control Conference 2024
Offline reinforcement learning (RL) is a promising approach for many control applications but faces challenges such as limited data coverage and value function overestimation. In this paper, we propose an implicit actor-critic (iAC) framework that em
Externí odkaz:
http://arxiv.org/abs/2408.15368
Publikováno v:
ICLR 2023
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has not been adequately addressed in the existing works, making their applic
Externí odkaz:
http://arxiv.org/abs/2405.16601
Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to external modi operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities. Due to
Externí odkaz:
http://arxiv.org/abs/2308.10379
Autor:
Khattar, Vanshaj, Jin, Ming
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplif
Externí odkaz:
http://arxiv.org/abs/2212.01939
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of solution functions of linear programming (LP) and quadratic programmin
Externí odkaz:
http://arxiv.org/abs/2212.01314
Autor:
Khattar, Vanshaj
Threat assessment and reliable motion-prediction of surrounding vehicles are some of the major challenges encountered in autonomous vehicles' safe decision-making. Predicting a threat in advance can give an autonomous vehicle enough time to avoid cra
Externí odkaz:
http://hdl.handle.net/10919/103470
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