Zobrazeno 1 - 10
of 194
pro vyhledávání: '"Cao, Yongcan"'
In this paper, we propose an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems, subject to bounded uncertainties, characterized by complex interactions. Specifically, the proposed method is capable of joint
Externí odkaz:
http://arxiv.org/abs/2409.19769
In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer while ensuring the pursuer's safety by preventing collisions with the target. The proposed guidance strategy steers the pursuer to
Externí odkaz:
http://arxiv.org/abs/2404.16312
This paper introduces an approach to address the target enclosing problem using non-holonomic multiagent systems, where agents self-organize on the enclosing shape around a fixed target. In our approach, agents independently move toward the desired e
Externí odkaz:
http://arxiv.org/abs/2404.04497
Autor:
White, Devin, Wu, Mingkang, Novoseller, Ellen, Lawhern, Vernon J., Waytowich, Nicholas, Cao, Yongcan
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, base
Externí odkaz:
http://arxiv.org/abs/2307.16348
In this paper, we address the issue of fairness in preference-based reinforcement learning (PbRL) in the presence of multiple objectives. The main objective is to design control policies that can optimize multiple objectives while treating each objec
Externí odkaz:
http://arxiv.org/abs/2306.09995
Human demonstrations can provide trustful samples to train reinforcement learning algorithms for robots to learn complex behaviors in real-world environments. However, obtaining sufficient demonstrations may be impractical because many behaviors are
Externí odkaz:
http://arxiv.org/abs/2010.07467
In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve data-target association problem when targets move on a constrained space and minimal information of the
Externí odkaz:
http://arxiv.org/abs/2009.09925
Autor:
Tao, Feng, Cao, Yongcan
In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent. One main relevant approach is the inve
Externí odkaz:
http://arxiv.org/abs/2009.09577
Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For example, employing deep neural networks on mobile systems requires the de
Externí odkaz:
http://arxiv.org/abs/1912.02254
Autor:
Zhan, Huixin, Cao, Yongcan
Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives. A typical approach to obtain optimal policies is to fir
Externí odkaz:
http://arxiv.org/abs/1910.01919