6 papers
Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
Magnus Victor Boock, Abdullah Akgül, Mustafa Mert Ãelikok +1
We present a new operator-theoretic representation learning framework for offline reinforcement learning that recovers the directed temporal geometry of a controlled Markov process…
Distributional Active Inference
Abdullah Akgül, Abdullah Akgül, Gulcin Baykal +5
Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted…
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
Abdullah Akgül, Gulcin Baykal, Manuel HauÃmann +1
Continuous control of non-stationary environments is a major challenge for deep reinforcement learning algorithms. The time-dependency of the state transition dynamics aggravates t…
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…
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…
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
Abdullah Akgül, Manuel HauÃmann, Melih Kandemir
Current approaches to model-based offline reinforcement learning often incorporate uncertainty-based reward penalization to address the distributional shift problem. These approach…