6 citations · 14 across the 8 of their papers we have counts for
7 papers · 1 filter
TROLL: Trust Regions improve Reinforcement Learning for Large Language Models
Philipp Becker, Niklas Freymuth, Serge Thilges +2
Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). Although recent work has expl…
Efficient Off-Policy Learning for High-Dimensional Action Spaces
Fabian Otto, Philipp Becker, Ngo Anh Vien +1
Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spac…
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Ge Li, Hongyi Zhou, Dominik Roth +4
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods e…
MP3: Movement Primitive-Based (Re-)Planning Policy
Fabian Otto, Hongyi Zhou, Onur Celik +3
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL fram…
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL
Philipp Becker, Sebastian Mossburger, Fabian Otto +1
Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning…
Deep Black-Box Reinforcement Learning with Movement Primitives
Fabian Otto, Onur Celik, Hongyi Zhou +3
\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a contro…
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Ngo Anh Vien +2
Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust region…