Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Stump, Ethan"'
Autor:
Kurtz, Martina Stadler, Prentice, Samuel, Veys, Yasmin, Quang, Long, Nieto-Granda, Carlos, Novitzky, Michael, Stump, Ethan, Roy, Nicholas
We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the en
Externí odkaz:
http://arxiv.org/abs/2404.17438
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructured off-road environments. In contrast to most decision-theoretic plann
Externí odkaz:
http://arxiv.org/abs/2111.08748
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it
Externí odkaz:
http://arxiv.org/abs/2107.12254
We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space. We improve the sample complexity of this approach by imposing a structure of the state-action function through a norma
Externí odkaz:
http://arxiv.org/abs/2103.14474
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward s
Externí odkaz:
http://arxiv.org/abs/1906.02671
Autor:
Kott, Alexander, Stump, Ethan
Publikováno v:
In Artificial Intelligence for the Internet of Everything, pp. 47-65. Academic Press, 2019
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This chapter explores the characteris
Externí odkaz:
http://arxiv.org/abs/1902.10086
We consider Markov Decision Problems defined over continuous state and action spaces, where an autonomous agent seeks to learn a map from its states to actions so as to maximize its long-term discounted accumulation of rewards. We address this proble
Externí odkaz:
http://arxiv.org/abs/1804.07323
We consider policy evaluation in infinite-horizon discounted Markov decision problems (MDPs) with infinite spaces. We reformulate this task a compositional stochastic program with a function-valued decision variable that belongs to a reproducing kern
Externí odkaz:
http://arxiv.org/abs/1709.04221
Publikováno v:
Hand Therapy; Sep2024, Vol. 29 Issue 3, p135-139, 5p
Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage they require. To solve this problem in a memory-affordab
Externí odkaz:
http://arxiv.org/abs/1612.04111