1 citations · 1 across the 4 of their papers we have counts for
6 papers
ObjectRL: An Object-Oriented Reinforcement Learning Codebase
Gulcin Baykal, Abdullah Akgül, Manuel Haussmann +4
ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebas…
Adaptive Ensemble Aggregation for Actor-Critics
Nicklas Werge, Yi-Shan Wu, Manuel Haussmann +2
Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or ta…
Deep Actor-Critics with Tight Risk Certificates
Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu +2
Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user fe…
Improving Actor-Critic Training with Steerable Action-Value Approximation Errors
Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu +1
Off-policy actor-critic algorithms have shown strong potential in deep reinforcement learning for continuous control tasks. Their success primarily comes from leveraging pessimisti…
Deep Exploration with PAC-Bayes
Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge +2
Reinforcement learning (RL) for continuous control under delayed rewards is an under-explored problem despite its significance in real-world applications. Many complex skills are b…
PAC-Bayesian Soft Actor-Critic Learning
Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann +2
Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy evaluation and improvement via two separate function approximators. The practicality of this a…