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pro vyhledávání: '"Schlaginhaufen, Andreas"'
Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations, motivated by the idea that the reward, rather than the policy, is the most succinct and transferable description of a task [Ng et al., 2000]. However, the reward
Externí odkaz:
http://arxiv.org/abs/2406.01793
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a st
Externí odkaz:
http://arxiv.org/abs/2403.16829
Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in c
Externí odkaz:
http://arxiv.org/abs/2306.00629
Publikováno v:
Advances in Neural Information Processing Systems, 2021
Learning how complex dynamical systems evolve over time is a key challenge in system identification. For safety critical systems, it is often crucial that the learned model is guaranteed to converge to some equilibrium point. To this end, neural ODEs
Externí odkaz:
http://arxiv.org/abs/2110.14296
Autor:
Schlaginhaufen, Andreas
Bridging the gap between deep learning and dynamical systems, neural ODEs are a promising approach to model continuous-time dynamical systems. Motivated by state augmentation in discrete-time models, we propose to extend the neural ODE framework to n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4491aecfc10cd4216dcc525a039fae2d
Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::372fc47f789333f913790673d82c4566
https://infoscience.epfl.ch/record/302138
https://infoscience.epfl.ch/record/302138