Planning with Diffusion for Flexible Behavior Synthesis (2205.09991v2)

Abstract: Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility.

• The paper introduces a diffusion-based planning method that iteratively denoises trajectories to merge model learning with decision making.
• It models complete trajectories non-autoregressively, reducing error accumulation and enhancing planning over long horizons.
• The framework is task-agnostic, demonstrating robust performance in tasks such as maze navigation and block stacking.

Planning with Diffusion for Flexible Behavior Synthesis

The paper introduces a novel approach combining model-based reinforcement learning with diffusion probabilistic models to achieve effective behavior synthesis. The approach challenges the conventional separation between model learning and trajectory optimization by integrating these processes within a diffusion-based framework.

Key Contributions

1. Diffusion-Based Planning: The core of the proposed method is the use of a diffusion probabilistic model, called Diffuser, which performs trajectory planning by iteratively denoising trajectories. This reformulation allows for planning and model sampling to converge, creating a seamless transition between model learning and decision making.
2. Trajectory-Level Modeling: Unlike traditional autoregressive models, Diffuser predicts complete trajectories non-autoregressively, enhancing planning capabilities over long horizons. This is particularly advantageous in settings where autoregressive predictions are prone to compounding errors.
3. Flexibility and Scalability: The model is designed to be task-agnostic, enabling its use across different tasks without retraining. This flexibility is achieved by separating the model from reward functions during training, allowing for new tasks to be addressed through the guide functions used during the diffusion process.
4. Guided Sampling and Inpainting: The paper introduces methodologies for goal-conditioned sampling (using classifier-guidance) and constraint satisfaction (using inpainting). This flexibility in trajectory sampling is illustrated through tasks that involve planning with sparse rewards and goal-specific constraints.

Numerical Results

The paper provides empirical evidence demonstrating the effectiveness of Diffuser in several environments:

• Maze Navigation: In long-horizon planning tasks, such as Maze2D and its variants, Diffuser outperformed several model-free and model-based baselines, demonstrating its superior capability to plan and execute sequences in sparse reward scenarios.
• Block Stacking: In tasks that test generalization to novel configurations, Diffuser showcased strong performance, outperforming previous offline reinforcement learning methods. This highlights its ability to adapt to new test-time goals effectively.
• Locomotion Tasks: In D4RL locomotion benchmarks, Diffuser delivered competitive results. While not always outperforming state-of-the-art methods, it showed robustness across diverse datasets.

Theoretical and Practical Implications

The paper presents theoretical implications for integrating denoising diffusion models within reinforcement learning, offering a new perspective on trajectory optimization. Practically, it introduces a scalable and flexible planning framework that can be adapted to a variety of tasks without extensive retraining.

The method's reliance on iterative denoising allows it to scale effectively with planning horizon length while maintaining robustness in long-horizon tasks. The decoupling of the model from specific reward structures promotes reusability across tasks, making it suitable for environments requiring adaptive planning strategies.

Future Directions

The paper positions itself as a stepping stone toward new methodologies in model-based reinforcement learning. Future research could explore enhancing diffusion models for richer state representations and exploring their applicability to broader domains, including real-time systems. Additionally, optimization of computational efficiency through advanced warm-start techniques could render this approach even more practical for real-world applications.

In summary, the paper successfully introduces an innovative integration of diffusion models into reinforcement learning, providing a compelling alternative for trajectory-based planning that offers both flexibility and robustness.