Efficient RL
Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing complex simulators while improving the sim2real transition of models, explicitly modelling the environment, or leveraging available offline data.

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Pill

Critic Regularized Regression

A simple but powerful algorithm for offline reinforcement learning, which can be seen as a combination of behavior cloning and Q-learning, …

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Efficient RL
Pill

Deep Reinforcement Learning at the Edge of the Statistical Precipice

A sound statistical analysis of the performance of RL algorithms is performed, with outcomes that force a reconsideration of the ranking of …

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Efficient RL
Pill

Planning with Diffusion for Flexible Behavior Synthesis

Diffusion models are finding their way into more machine learning domains. This paper introduces a framework for applying them to …

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Efficient RL
Diffusion Models
Training

Safe and efficient deep reinforcement learning

This 2-day dive into deep RL for decision and control is suitable for engineers who want to solve real-world control problems with efficient …

Training

Classical and modern methods in planning and control

A review of model-based planning and control for engineers interested in solving real-world decision and control problems with efficient …

Blog

Natural, Trust Region and Proximal Policy Optimization

We present an overview of the theory behind three popular and related algorithms for gradient based policy optimization: natural policy …

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Efficient RL
Seminar

Problems and solutions in offline learning

Current reinforcement learning algorithms require tons of data to solve new tasks. This “hunger for data” is especially onerous …

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Efficient RL
Seminar

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Paper abstract: Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, …

Fabian Hainzl
Data Efficiency
Efficient RL
Seminar

A short introduction to model-based Reinforcement Learning

Model-based RL learns a deterministic or probabilistic forward model of the dynamics of an environment for action planning or direct policy …

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Data Efficiency
Efficient RL

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Data valuation

Attributions of value to training samples can be used to examine data, improve data acquisition, debug and improve models or compensate providers. Recent developments in the field enable principled and useful definitions of value which …

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Representation Learning

Representation learning is concerned with finding semantically useful and compact, i.e., low dimensional, representations for inputs from selected data distributions. These representations can be efficiently used for downstream tasks, …

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Data efficiency

Numerous applications of Machine Learning in business intelligence, process optimization, product development or sales do not benefit from true “big data”. Blind application of state-of-the-art methods can lead to catastrophic …

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