6 citations · 15 across the 8 of their papers we have counts for
8 papers
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Robert Tjarko Lange, Henning Sprekeler
Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly s…
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization
Robert Tjarko Lange, Tom Schaul, Yutian Chen +4
Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpos…
evosax: JAX-based Evolution Strategies
Robert Tjarko Lange
The deep learning revolution has greatly been accelerated by the 'hardware lottery': Recent advances in modern hardware accelerators and compilers paved the way for large-scale bat…
Discovering Evolution Strategies via Meta-Black-Box Optimization
Robert Tjarko Lange, Tom Schaul, Yutian Chen +5
Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies. While highly general, their learning dynamics are often times heuri…
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
Marc Aurel Vischer, Robert Tjarko Lange, Henning Sprekeler
The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distribu…
Learning Not to Learn: Nature versus Nurture in Silico
Robert Tjarko Lange, Henning Sprekeler
Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time,…
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions
Petros Christodoulou, Robert Tjarko Lange, Ali Shafti +1
From a young age humans learn to use grammatical principles to hierarchically combine words into sentences. Action grammars is the parallel idea, that there is an underlying set of…
Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions
Robert Tjarko Lange, Aldo Faisal
Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of-the-art algorithm…