8 papers
Progress Constraints for Reinforcement Learning in Behavior Trees
Finn Rietz, Mart Kartašev, Petter Ögren +1
Behavior Trees (BTs) provide a structured and reactive framework for decision-making, commonly used to switch between sub-controllers based on environmental conditions. Reinforceme…
APC-RL: Exceeding Data-Driven Behavior Priors with Adaptive Policy Composition
Finn Rietz, Pedro Zuidberg dos Martires, Johannes Andreas Stork
Incorporating demonstration data into reinforcement learning (RL) can greatly accelerate learning, but existing approaches often assume demonstrations are optimal and fully aligned…
Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompting has emerged as the dominant paradigm for adapting large, pre-trained transformer-based models to downstream tasks. The Prompting Decision Transformer (PDT) enables large-…
Prompt Tuning Decision Transformers with Structured and Scalable Bandits
Finn Rietz, Oleg Smirnov, Sara Karimi +1
Prompt tuning has emerged as a key technique for adapting large pre-trained Decision Transformers (DTs) in offline Reinforcement Learning (RL), particularly in multi-task and few-s…
Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies
Finn Rietz, Erik Schaffernicht, Stefan Heinrich +1
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both…
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer
Finn Rietz, Johannes Andreas Stork
Discovering all useful solutions for a given task is crucial for transferable RL agents, to account for changes in the task or transition dynamics. This is not considered by classi…