Mean Flow Policy Optimization
For researchers in reinforcement learning, MFPO offers a more efficient alternative to diffusion-based policy representations, addressing a key bottleneck in computational overhead.
Mean Flow Policy Optimization (MFPO) uses MeanFlow models to represent policies in online reinforcement learning, achieving comparable or better performance than diffusion-based methods while significantly reducing training and inference time.
Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative processes introduce substantial training and inference overhead. To overcome this limitation, we propose to represent policies using MeanFlow models, a class of few-step flow-based generative models, to improve training and inference efficiency over diffusion-based RL approaches. To promote exploration, we optimize MeanFlow policies under the maximum entropy RL framework via soft policy iteration, and address two key challenges specific to MeanFlow policies: action likelihood evaluation and soft policy improvement. Experiments on MuJoCo and DeepMind Control Suite benchmarks demonstrate that our method, Mean Flow Policy Optimization (MFPO), achieves performance comparable to or exceeding current diffusion-based baselines while considerably reducing training and inference time. Our code is available at https://github.com/MFPolicy/MFPO.