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A fairly reliable trend in deep reinforcement learning is that theperformance scales with the number of parameters, provided a complimentaryscaling in amount of training data. As the appetite for large models increases,it is imperative to address, sooner than later, the potential problem ofrunning out of high-quality demonstrations. In this case, instead of collectingonly new data via costly human demonstrations or risking a simulation-to-realtransfer with uncertain effects, it would be beneficial to leverage vastamounts of readily-available low-quality data. Since classical controlalgorithms such as behavior cloning or temporal difference learning cannot beused on reward-free or action-free data out-of-the-box, this solution warrantsnovel training paradigms for continuous control. We propose a simple algorithmcalled Diffused Value Function (DVF), which learns a joint multi-step model ofthe environment-robot interaction dynamics using a diffusion model. This modelcan be efficiently learned from state sequences (i.e., without access to rewardfunctions nor actions), and subsequently used to estimate the value of eachaction out-of-the-box. We show how DVF can be used to efficiently capture thestate visitation measure for multiple controllers, and show promisingqualitative and quantitative results on challenging robotics benchmarks.

Value function estimation using conditional diffusion models for control