The field of reinforcement learning is moving towards safer and more reliable methods, with a focus on real-world applications and complex decision-making problems. Researchers are developing new frameworks and algorithms that can handle partial state information, safety constraints, and industrial complexity. One of the key areas of innovation is the integration of reinforcement learning with model-based approaches, which enables agents to plan and optimize actions while ensuring safety and reliability. Another important trend is the development of benchmark suites and testbeds that can evaluate the performance of safe reinforcement learning algorithms in practical and challenging environments. Notable papers in this area include: DATD3, which introduces a novel actor-critic algorithm for model-free reinforcement learning under output feedback control. SafeOR-Gym, a benchmark suite for safe reinforcement learning algorithms on practical operations research problems. A Lyapunov Drift-Plus-Penalty Method, which adapts the Lyapunov Drift-Plus-Penalty algorithm for reinforcement learning applications with queue stability. Safe Planning and Policy Optimization via World Model Learning, which proposes a novel model-based RL framework that jointly optimizes task performance and safety.