My current research focuses on the incorporation of probabilistic state transition models in reinforcement learning algorithms. Model-based RL agents generalize from past experience very effectively, allowing the agent to evaluate policies with fewer environment interactions than their model-free counterparts. Improving the data-efficiency of RL agents is crucial for real-world applications in fields like robotics, logistics, and finance.