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pro vyhledávání: '"Stork, Johannes Andreas"'
Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant variables. We demonstrate in ex
Externí odkaz:
http://arxiv.org/abs/2407.20003
Autor:
Rietz, Finn, Stork, Johannes Andreas
Discovering all useful solutions for a given task is crucial for transferable RL agents, to account for changes in the task or transition dynamics. This is not considered by classical RL algorithms that are only concerned with finding the optimal pol
Externí odkaz:
http://arxiv.org/abs/2310.07493
Reinforcement learning (RL) for complex tasks remains a challenge, primarily due to the difficulties of engineering scalar reward functions and the inherent inefficiency of training models from scratch. Instead, it would be better to specify complex
Externí odkaz:
http://arxiv.org/abs/2310.02360
Publikováno v:
Publikationer från Örebro universitet.
Tracking deformable linear objects (DLOs) is a key element for applications where robots manipulate DLOs. However, the lack of distinctive features or appearance on the DLO and the object’s high-dimensional state space make tracking challenging and
Publikováno v:
Publikationer från Örebro universitet.
Reinforcement Learning (RL) has the potential of solving complex continuous control tasks, with direct applications to robotics. Nevertheless, current state-of-the-art methods are generally unsafe to learn directly on a physical robot as exploration
Autor:
Stork, Johannes Andreas
Robots need to grasp, handle, and manipulate objects, navigate their environment, and understand the state of the world around them. Like all artificial intelligence agents, they have to make predictions, formulate goals, reason about actions, and ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2406e23ebe5afff91da22cd84d810006
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186424
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186424