Options hedging in real-world markets is challenged by strong asset correlations, partial observability, and nonstationary price dynamics. These conditions limit the effectiveness of traditional strategies and pose challenges for reinforcement learning (RL) methods, which often overfit or become unstable in high-dimensional environments. Existing RL hedging methods suffer from static risk measures and inflexible policies, leading to issues in stability, adaptability, and generalization. To address these challenges, we propose Deep Diffusion Reinforcement Learning (DDRL), a new RL framework that integrates the Soft Actor-Critic (SAC) algorithm with diffusion-based generative policy networks for dynamic hedging. By modeling policy distributions through a denoising diffusion process, DDRL effectively captures nonlinear dependencies and generates more robust actions. To improve stability, DDRL also incorporates double critic networks, entropy regularization, soft target updates, and other technical enhancements. We implement DDRL in a simulated trading environment based on historical market data. Empirical results show that DDRL reduces hedging losses and transaction costs compared to baseline RL methods, while maintaining stable performance across varied portfolio configurations and market conditions. These results highlight the potential of generative diffusion policies to enhance the robustness and reliability of RL-based financial decision-making.