Diffused Task-Agnostic Milestone Planner
Mineui Hong Minjae Kang Songhwai Oh
Addressing decision-making problems using sequence modeling to predict future
trajectories shows promising results in recent years. In this paper, we take a
step further to leverage the sequence predictive method in wider areas such as
long-term planning, vision-based control, and multi-task decision-making. To
this end, we propose a method to utilize a diffusion-based generative sequence
model to plan a series of milestones in a latent space and to have an agent to
follow the milestones to accomplish a given task. The proposed method can learn
control-relevant, low-dimensional latent representations of milestones, which
makes it possible to efficiently perform long-term planning and vision-based
control. Furthermore, our approach exploits generation flexibility of the
diffusion model, which makes it possible to plan diverse trajectories for
multi-task decision-making. We demonstrate the proposed method across offline
reinforcement learning (RL) benchmarks and an visual manipulation environment.
The results show that our approach outperforms offline RL methods in solving
long-horizon, sparse-reward tasks and multi-task problems, while also achieving
the state-of-the-art performance on the most challenging vision-based
manipulation benchmark.