Model Predictive Control with Differentiable World Models for Offline Reinforcement Learning

Rohan Deb, Stephen J. Wright, Arindam Banerjee

Keywords: MPC, Offline RL, World Models

TL;DR: Offline RL, but at test time we gradient-update the policy on the fly through a differentiable world model (MPC-style imagined rollouts) before acting, boosting D4RL performance.

Abstract: Offline Reinforcement Learning (RL) aims to learn optimal policies from fixed offline datasets, without further interactions with the environment. Such methods train an offline policy (or value function), and apply it at inference time without further refinement. We introduce an inference time adaptation framework inspired by model predictive control (MPC) that utilizes a pretrained policy along with a learned world model of state transitions and rewards. While existing world model and diffusion-planning methods use learned dynamics to generate imagined trajectories during training, or to sample candidate plans at inference time, they do not use inference-time information to optimize the policy parameters on the fly. In contrast, our design is a Differentiable World Model (DWM) pipeline that enables end-to-end gradient computation through imagined rollouts for policy optimization at inference time based on MPC. We evaluate our algorithm on D4RL continuous-control benchmarks (MuJoCo locomotion tasks and AntMaze), and show that exploiting inference-time information to optimize the policy parameters yields consistent gains over strong offline RL baselines.

Submission Number: 77