Advancements in reinforcement learning have paved the way for more sophisticated policies for decision-making in complex environments. This project delves into the exploration of diffusion policies, a class of algorithms that leverage stochastic processes to model the decision-making process in both discrete and continuous action spaces. The primary objective is to design, implement, and evaluate diffusion policies for a range of applications, showcasing their adaptability and effectiveness in diverse scenarios. The student will be responsible for implementing the method, conducting experiments, and compiling the results into a paper.