2 citations · 3 across the 6 of their papers we have counts for
6 papers
Discrete Variational Autoencoding via Policy Search
Michael Drolet, Firas Al-Hafez, Aditya Bhatt +2
Discrete latent bottlenecks in variational autoencoders (VAEs) offer high bit efficiency and can be modeled with autoregressive discrete distributions, enabling parameter-efficient…
The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control
Oleg Kaidanov, Firas Al-Hafez, Yusuf Suvari +2
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 o…
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion
Henri-Jacques Geiß, Firas Al-Hafez, Andre Seyfarth +2
Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods atte…
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
Firas Al-Hafez, Guoping Zhao, Jan Peters +1
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks,…
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
Firas Al-Hafez, Guoping Zhao, Jan Peters +1
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly…
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
Firas Al-Hafez, Davide Tateo, Oleg Arenz +2
Recent methods for imitation learning directly learn a -function using an implicit reward formulation rather than an explicit reward function. However, these methods generally r…