52 citations · 82 across the 6 of their papers we have counts for
11 papers
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska +2
Optimal decision-making under partial observability requires reasoning about the uncertainty of the environment's hidden state. However, most reinforcement learning architectures h…
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning
Melrose Roderick, Gaurav Manek, Felix Berkenkamp +1
A key problem in off-policy Reinforcement Learning (RL) is the mismatch, or distribution shift, between the dataset and the distribution over states and actions visited by the lear…
Information-Theoretic Safe Exploration with Gaussian Processes
Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska +2
We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a…
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi, Felix Berkenkamp, Andreas Krause
Model-based reinforcement learning algorithms with probabilistic dynamical models are amongst the most data-efficient learning methods. This is often attributed to their ability to…
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp +6
Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience…