Reinforcement Learning
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

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 …

Michael Panchenko
Reinforcement Learning
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 …

Fabio Peruzzo
Reinforcement Learning
Diffusion Models
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 …

Michael Panchenko
Reinforcement Learning
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 in high …

Robert Müller
Reinforcement Learning
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
Reinforcement Learning
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 …

Miguel de Benito Delgado
Data Efficiency
Reinforcement Learning

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