The field of reinforcement learning is moving towards addressing the challenges of uncertainty and robustness in dynamic environments. Recent developments focus on improving the scalability and efficiency of model-based reinforcement learning, as well as enhancing the generalizability of reinforcement learning agents. Notable advancements include the integration of physics-informed models, uncertainty-aware dynamics models, and optimistic exploration strategies. These innovations have shown promising results in various applications, including control tasks and open-pit mining optimization. Some noteworthy papers include: SOMBRL, which proposes a scalable and optimistic model-based reinforcement learning approach with sublinear regret for nonlinear dynamics. Deep Gaussian Process Proximal Policy Optimization, which introduces a scalable, model-free actor-critic algorithm that leverages Deep Gaussian Processes to approximate both the policy and value function, providing well-calibrated uncertainty estimates.