Zobrazeno 1 - 5
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pro vyhledávání: '"Ovinnikov, Ivan"'
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations. The commonplace scarcity and heterogeneous sources of such demonstrations can lead to the absorption
Externí odkaz:
http://arxiv.org/abs/2409.08012
Autor:
Ovinnikov, Ivan, Buhmann, Joachim M.
Imitation learning methods are used to infer a policy in a Markov decision process from a dataset of expert demonstrations by minimizing a divergence measure between the empirical state occupancy measures of the expert and the policy. The guiding sig
Externí odkaz:
http://arxiv.org/abs/2308.09189
The goal of inverse reinforcement learning (IRL) is to infer a reward function that explains the behavior of an agent performing a task. The assumption that most approaches make is that the demonstrated behavior is near-optimal. In many real-world sc
Externí odkaz:
http://arxiv.org/abs/2011.09264
Autor:
Ovinnikov, Ivan
Publikováno v:
Bayesian Deep Learning Workshop (NeurIPS 2018)
This work presents a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincar\'e ball model of the hyperbolic space. By assuming the latent space to be hyperbolic, we can use its intrinsic hier
Externí odkaz:
http://arxiv.org/abs/1901.01427
Publikováno v:
International Journal of Computer Assisted Radiology & Surgery; Sep2024, Vol. 19 Issue 9, p1773-1781, 9p
