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pro vyhledávání: '"Nashed, Samer B."'
Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. However, they show poor asymptotic performance and struggle with out-of
Externí odkaz:
http://arxiv.org/abs/2306.15909
As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research commu
Externí odkaz:
http://arxiv.org/abs/2301.05753
We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reasoning. This single framework can identify multiple, semantically distin
Externí odkaz:
http://arxiv.org/abs/2205.15462
Autor:
Nashed, Samer B.
As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary. Current state-of-the-art logging, playback, and exploration systems are insufficient for practitioners seeking to
Externí odkaz:
http://arxiv.org/abs/2007.15746
Autonomous vehicles (AVs) require accurate metric and topological location estimates for safe, effective navigation and decision-making. Although many high-definition (HD) roadmaps exist, they are not always accurate since public roads are dynamic, s
Externí odkaz:
http://arxiv.org/abs/1803.01378
Autor:
Nashed, Samer B., Biswas, Joydeep
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algori
Externí odkaz:
http://arxiv.org/abs/1711.08566
We present a novel framework for causal explanations of stochastic, sequential decision-making systems. Building on the well-studied structural causal model paradigm for causal reasoning, we show how to identify semantically distinct types of explana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95c3b6617ee8df331c99eddf949c8abb
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