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pro vyhledávání: '"Ramos, João A. Cândido"'
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be derived from various sources such as adversarial games, trajectory matchin
Externí odkaz:
http://arxiv.org/abs/2306.09805
In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects. These groups follow some common physical laws that exhibit specificities that are captured through some vectorial description. We develop a mo
Externí odkaz:
http://arxiv.org/abs/2106.11083
Imitation learning from demonstrations (ILD) aims to alleviate numerous shortcomings of reinforcement learning through the use of demonstrations. However, in most real-world applications, expert action guidance is absent, making the use of ILD imposs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97fab3b6c00347c2bcc39889b20cc7f9
http://arxiv.org/abs/2306.09805
http://arxiv.org/abs/2306.09805