4 papers
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
Nicklas Werge, Yi-Shan Wu, Abdullah Akgül +2
In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator. Because WRLS only provides poi…
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…
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…