Zobrazeno 1 - 10
of 22 798
pro vyhledávání: '"Sequential decision-making"'
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the parameters of cost functions, models, and constraints. Bayesian optimization is a common approach to learning these parameters
Externí odkaz:
http://arxiv.org/abs/2412.02423
Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems. In this paper, we investigate t
Externí odkaz:
http://arxiv.org/abs/2410.05656
Autor:
Nüßlein, Jonas, Zorn, Maximilian, Ritz, Fabian, Stein, Jonas, Stenzel, Gerhard, Schönberger, Julian, Gabor, Thomas, Linnhoff-Popien, Claudia
Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simulated environments), sensors are essential for measuring the current s
Externí odkaz:
http://arxiv.org/abs/2412.07686
Autor:
Yan, Ke
This work analyses the disparity in performance between Decision Transformer (DT) and Decision Mamba (DM) in sequence modelling reinforcement learning tasks for different Atari games. The study first observed that DM generally outperformed DT in the
Externí odkaz:
http://arxiv.org/abs/2412.00725
We address the challenge of explaining counterfactual outcomes in multi-agent Markov decision processes. In particular, we aim to explain the total counterfactual effect of an agent's action on the outcome of a realized scenario through its influence
Externí odkaz:
http://arxiv.org/abs/2410.12539
Autor:
Taitler, Ayal
Perimeter identification involves ascertaining the boundaries of a designated area or zone, requiring traffic flow monitoring, control, or optimization. Various methodologies and technologies exist for accurately defining these perimeters; however, t
Externí odkaz:
http://arxiv.org/abs/2409.02549
We propose a general approach to quantitatively assessing the risk and vulnerability of artificial intelligence (AI) systems to biased decisions. The guiding principle of the proposed approach is that any AI algorithm must outperform a random guesser
Externí odkaz:
http://arxiv.org/abs/2407.20276
Autor:
Chen, Xianfu, Wu, Celimuge, Shen, Yi, Ji, Yusheng, Yoshinaga, Tsutomu, Ni, Qiang, Zarakovitis, Charilaos C., Zhang, Honggang
This article investigates a control system within the context of six-generation wireless networks. The control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control su
Externí odkaz:
http://arxiv.org/abs/2407.06227
Autor:
Qin, Zhanyue, Wang, Haochuan, Liu, Deyuan, Song, Ziyang, Fan, Cunhang, Lv, Zhao, Wu, Jinlin, Lei, Zhen, Tu, Zhiying, Chu, Dianhui, Yu, Xiaoyan, Sui, Dianbo
Sequential decision-making refers to algorithms that take into account the dynamics of the environment, where early decisions affect subsequent decisions. With large language models (LLMs) demonstrating powerful capabilities between tasks, we can't h
Externí odkaz:
http://arxiv.org/abs/2406.16382
This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs. The former approach suff
Externí odkaz:
http://arxiv.org/abs/2406.12125