17 citations · 54 across the 21 of their papers we have counts for
12 papers · 1 filter
A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning
Anish Diwan, Jan Peters, Oleg Arenz
Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient descent to obtain the optimal re…
Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
Anish Diwan, Davide Tateo, Christopher E. Mower +3
Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classical (dual-ascent) IRL guarante…
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…
Maximum Total Correlation Reinforcement Learning
Bang You, Puze Liu, Huaping Liu +2
Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward fu…
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential e…
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…
Rollout Total Correlation for Deep Reinforcement Learning
Bang You, Huaping Liu, Jingming Xie +3
Learning task-relevant representations is crucial for reinforcement learning. Recent approaches aim to learn such representations by improving the temporal consistency in the obser…
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Oleg Arenz, Philipp Dahlinger, Zihan Ye +2
Variational inference with Gaussian mixture models (GMMs) enables learning of highly tractable yet multi-modal approximations of intractable target distributions with up to a few h…
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images
Bang You, Oleg Arenz, Youping Chen +1
Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent's policy or Q-function. In particular, methods based on…
Non-Adversarial Imitation Learning and its Connections to Adversarial Methods
Oleg Arenz, Gerhard Neumann
Many modern methods for imitation learning and inverse reinforcement learning, such as GAIL or AIRL, are based on an adversarial formulation. These methods apply GANs to match the…
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Philipp Becker, Oleg Arenz, Gerhard Neumann
Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection…
Trust-Region Variational Inference with Gaussian Mixture Models
Oleg Arenz, Mingjun Zhong, Gerhard Neumann
Many methods for machine learning rely on approximate inference from intractable probability distributions. Variational inference approximates such distributions by tractable model…