What Makes a Good Diffusion Planner for Decision Making?
This work addresses the inconsistent design choices in diffusion planning for offline reinforcement learning, providing practical insights to improve performance in decision-making tasks.
The authors systematically investigated the key components of diffusion planners for decision-making, revealing that unconventional design choices like unconditional sampling with selection and Transformer networks outperform common practices, leading to a new baseline that achieves state-of-the-art results on offline RL benchmarks.
Diffusion models have recently shown significant potential in solving decision-making problems, particularly in generating behavior plans -- also known as diffusion planning. While numerous studies have demonstrated the impressive performance of diffusion planning, the mechanisms behind the key components of a good diffusion planner remain unclear and the design choices are highly inconsistent in existing studies. In this work, we address this issue through systematic empirical experiments on diffusion planning in an offline reinforcement learning (RL) setting, providing practical insights into the essential components of diffusion planning. We trained and evaluated over 6,000 diffusion models, identifying the critical components such as guided sampling, network architecture, action generation and planning strategy. We revealed that some design choices opposite to the common practice in previous work in diffusion planning actually lead to better performance, e.g., unconditional sampling with selection can be better than guided sampling and Transformer outperforms U-Net as denoising network. Based on these insights, we suggest a simple yet strong diffusion planning baseline that achieves state-of-the-art results on standard offline RL benchmarks.