Stateful policies play an important role in reinforcement learning, such as handling partially
observable environments, enhancing robustness, or imposing an inductive bias directly into …

Recent methods for imitation learning directly learn a $Q$-function using an implicit reward
formulation rather than an explicit reward function. However, these methods generally …

Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
However, many existing locomotion benchmarks primarily focus on simplified toy tasks, …

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation
and high-dimensional action space. While many reinforcement learning methods …

Humanoids have the potential to be the ideal embodiment in environments designed for
humans. Thanks to the structural similarity to the human body, they benefit from rich sources of …

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation
and high-dimensional action space. While many reinforcement learning methods …

We propose a novel approach that biases actions during policy search by lifting the concept
of redundancy resolution from multi-DoF robot kinematics to the level of the reward in deep …

Recent methods for imitation learning directly learn a $Q$-function using an implicit reward
formulation rather than an explicit reward function. However, these methods generally …

The Ministry of Finance (MoF) of the Syrian Arab Republic has requested advisory services
from the European Investment Bank (EIB) with a view to expanding medium and long-term …

The main focus of this article is the political discrimination and/or exclusion of Christian
minorities in Syria, with a particular focus on the era of Hafez al-Assad. It begins by analyzing the …