The field of reinforcement learning is moving towards developing more robust and risk-aware methods, with a focus on addressing the challenges of uncertainty and safety in complex environments. Recent research has explored the use of distributional reinforcement learning, probabilistic shielding, and robust multi-objective optimization to improve the performance and reliability of reinforcement learning algorithms. Notably, the development of novel uncertainty sets, such as elliptic uncertainty sets, has enabled more efficient and tractable robust policy evaluation. Additionally, the integration of risk-averse objectives, such as optimized certainty equivalents, has shown promise in high-stakes applications. Overall, these advances are expected to have a significant impact on the development of more reliable and efficient reinforcement learning systems. Noteworthy papers include: ES-C51, which proposes a modified version of the C51 algorithm using an Expected Sarsa update to improve stability and performance. ProSh, which introduces a model-free algorithm for safe reinforcement learning under cost constraints, providing formal guarantees about safety. RMOEA-UPF, which proposes a novel Uncertainty-related Pareto Front framework for robust multi-objective optimization, demonstrating consistent top-ranking performance on benchmark problems. D2C-HRHR, which formally defines high-risk-high-return tasks and proposes a reinforcement learning framework that discretizes continuous action spaces and employs entropy-regularized exploration to improve coverage of risky but rewarding actions. An Empirical Study of Lagrangian Methods in Safe Reinforcement Learning, which analyzes the optimality and stability of Lagrange multipliers in safe reinforcement learning and provides lambda-profiles to visualize the trade-off between return and constraint cost. Efficient Algorithms for Mitigating Uncertainty and Risk in Reinforcement Learning, which proposes the Coordinate Ascent Dynamic Programming algorithm to compute a Markov policy that maximizes the discounted return averaged over uncertain models. Robust Reinforcement Learning in Finance, which develops a novel class of elliptic uncertainty sets to model market impact and establishes both implicit and explicit closed-form solutions for the worst-case uncertainty. Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents, which proposes a framework for risk-aware constrained RL using optimized certainty equivalents and yields a simple algorithmic recipe that can be wrapped around standard RL solvers.