Zobrazeno 1 - 10
of 260
pro vyhledávání: '"Zavlanos, Michael M"'
Vision-based imitation learning has shown promising capabilities of endowing robots with various motion skills given visual observation. However, current visuomotor policies fail to adapt to drastic changes in their visual observations. We present Pe
Externí odkaz:
http://arxiv.org/abs/2406.19971
Autor:
Wang, Siyi, Wang, Zifan, Yi, Xinlei, Zavlanos, Michael M., Johansson, Karl H., Hirche, Sandra
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of chan
Externí odkaz:
http://arxiv.org/abs/2404.02988
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measur
Externí odkaz:
http://arxiv.org/abs/2403.10399
The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other hand, graph neural networks (GNN), neural architectures for processing d
Externí odkaz:
http://arxiv.org/abs/2402.03558
Autor:
Wang, Zifan, Gao, Yulong, Wang, Siyi, Zavlanos, Michael M., Abate, Alessandro, Johansson, Karl H.
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard reinforcement learni
Externí odkaz:
http://arxiv.org/abs/2401.10240
In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the abse
Externí odkaz:
http://arxiv.org/abs/2310.02384
Autor:
Riess, Hans, Henselman-Petrusek, Gregory, Munger, Michael C., Ghrist, Robert, Bell, Zachary I., Zavlanos, Michael M.
Preferences, fundamental in all forms of strategic behavior and collective decision-making, in their raw form, are an abstract ordering on a set of alternatives. Agents, we assume, revise their preferences as they gain more information about other ag
Externí odkaz:
http://arxiv.org/abs/2310.00179
Training robots with reinforcement learning (RL) typically involves heavy interactions with the environment, and the acquired skills are often sensitive to changes in task environments and robot kinematics. Transfer RL aims to leverage previous knowl
Externí odkaz:
http://arxiv.org/abs/2309.13753
Off-policy evaluation and learning are concerned with assessing a given policy and learning an optimal policy from offline data without direct interaction with the environment. Often, the environment in which the data are collected differs from the e
Externí odkaz:
http://arxiv.org/abs/2309.08748
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions and choose actions that best respond to
Externí odkaz:
http://arxiv.org/abs/2309.00307