The article introduces Differentiable Belief-based Opponent Shaping (D-BOS), a novel method in multi-agent reinforcement learning that enhances strategic interactions by shaping opponents' beliefs rather than just their actions. This approach shows improved performance in hidden-role games, particularly in mixed-motive scenarios, compared to existing methods like PPO and BBM.