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Model Predictive Control with Differentiable World Models for Offline Reinforcement Learning

arXiv:2603.22430v1 Announce Type: new 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 endto-end gradient computation through imagined rollouts for policy optimization at inference time based on MPC. We evaluate our algorithm o

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Rohan Deb, Stephen J. Wright, Arindam Banerjee · · 1 min read · 2 views
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arXiv:2603.22430v1 Announce Type: new 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 endto-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.

Executive Summary

This article introduces a novel approach to Offline Reinforcement Learning (RL) using Model Predictive Control (MPC) with a Differentiable World Model (DWM). The proposed method, DWM-MPC, leverages a pre-trained policy and a learned world model to optimize policy parameters at inference time. Unlike existing methods, DWM-MPC utilizes inference-time information to refine policy parameters, leading to improved performance on D4RL continuous-control benchmarks. The authors demonstrate consistent gains over strong offline RL baselines, highlighting the potential of DWM-MPC in real-world applications. The method's end-to-end gradient computation and ability to handle complex state transitions make it an attractive solution for offline RL challenges.

Key Points

  • ▸ DWM-MPC introduces a novel offline RL approach using MPC and DWM.
  • ▸ The method optimizes policy parameters at inference time using inferred information.
  • ▸ DWM-MPC achieves consistent gains over strong offline RL baselines on D4RL benchmarks.

Merits

Strength in Handling Complex State Transitions

The DWM-MPC method learns a differentiable world model, enabling the optimization of policy parameters in complex, high-dimensional state spaces.

Demerits

Computational Complexity

The method requires computationally expensive end-to-end gradient computations, which may limit its scalability in real-world applications.

Expert Commentary

While the DWM-MPC approach shows promise, its limitations, particularly in terms of computational complexity, must be carefully addressed. Furthermore, the method's applicability to more complex environments and real-world scenarios requires further investigation. Nevertheless, the idea of leveraging inference-time information to optimize policy parameters is a significant advancement in the field of offline RL. As researchers continue to explore and refine this approach, it may lead to breakthroughs in areas such as robotics, autonomous systems, and intelligent decision-making.

Recommendations

  • ✓ Future research should focus on reducing the computational complexity of DWM-MPC and exploring its applications in more complex environments.
  • ✓ Developing more efficient and scalable algorithms for offline RL is essential to unlock the full potential of methods like DWM-MPC.

Sources

Original: arXiv - cs.LG

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