Generating Synthetic On-Policy Trajectories for Offline Reinforcement Learning via Policy-Guided Diffusion

Core Concepts

Policy-guided diffusion generates synthetic trajectories that balance action likelihoods under both the target and behavior policies, leading to plausible trajectories with high target policy probability while retaining low dynamics error.

Abstract

The content discusses a method called policy-guided diffusion (PGD) for generating synthetic training data in offline reinforcement learning (RL) settings.
The key insights are:

Offline RL suffers from distribution shift between the behavior policy (which collected the offline data) and the target policy being trained. This leads to an out-of-sample issue where the target policy explores regions underrepresented in the offline data.
Prior work has proposed using autoregressive world models to generate synthetic on-policy experience. However, these models suffer from compounding error, forcing short rollouts that limit coverage.
PGD instead models entire trajectories using diffusion models, which avoid compounding error. It then applies guidance from the target policy to shift the sampling distribution towards high-likelihood actions under the target policy.
This yields a "behavior-regularized target distribution" that balances action likelihoods under both the behavior and target policies. This retains the benefits of diffusion (low dynamics error) while generating trajectories more representative of the target policy.
Experiments show that agents trained on PGD-generated synthetic data outperform those trained on real or unguided synthetic data, across a range of environments and behavior policies. PGD also achieves lower dynamics error than prior autoregressive world model approaches.

Stats

The content does not provide any specific numerical data or metrics. It focuses on describing the policy-guided diffusion method and comparing it qualitatively to prior approaches.

Quotes

The content does not contain any direct quotes that are particularly striking or support the key arguments.

Key Insights Distilled From

Policy-Guided Diffusion

by Matthew Thom... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06356.pdf

Deeper Inquiries

What are the theoretical guarantees or convergence properties of the behavior-regularized target distribution approximated by policy-guided diffusion

The behavior-regularized target distribution approximated by policy-guided diffusion offers certain theoretical guarantees and convergence properties. By balancing action likelihoods under both the behavior and target policies, the distribution aims to reduce dynamics error while maintaining high target policy probability. This regularization helps in mitigating the out-of-sample issue in offline reinforcement learning by guiding the synthetic trajectories towards a distribution that is a compromise between the behavior and target policies. The convergence properties of this distribution are based on the iterative denoising process of the diffusion model, which gradually adjusts the trajectory distribution towards the target policy while limiting divergence from the behavior policy. The theoretical guarantees stem from the optimization process that ensures the synthetic data generated by policy-guided diffusion is representative of the target policy while being anchored in the behavior distribution.

How could the policy guidance coefficient be automatically tuned to balance exploration of the target policy distribution and fidelity to the behavior policy

Automatically tuning the policy guidance coefficient in policy-guided diffusion is a crucial aspect that can enhance the balance between exploration of the target policy distribution and fidelity to the behavior policy. One approach to automatically tuning this coefficient could involve leveraging reinforcement learning techniques such as reinforcement learning with a learned reward function or reinforcement learning with intrinsic motivation. By incorporating a reward signal that incentivizes the model to generate trajectories that are both exploratory and aligned with the target policy, the guidance coefficient can be adjusted dynamically during training. Additionally, techniques like Bayesian optimization or grid search can be employed to search for the optimal value of the guidance coefficient that maximizes performance metrics such as target policy likelihood and dynamics error. This adaptive tuning mechanism can help in achieving a fine balance between exploration and exploitation in policy-guided diffusion.

Could policy-guided diffusion be extended to other generative modeling approaches beyond diffusion, such as variational autoencoders or generative adversarial networks, and how would the theoretical underpinnings change

Policy-guided diffusion can potentially be extended to other generative modeling approaches beyond diffusion, such as variational autoencoders (VAEs) or generative adversarial networks (GANs), with some adjustments to the theoretical underpinnings. In the case of VAEs, the policy guidance mechanism would need to be integrated into the latent space representation learning process to ensure that the generated samples align with the target policy distribution while being anchored in the behavior policy distribution. This would involve incorporating the policy guidance signal into the loss function of the VAE to encourage the generation of trajectories that are consistent with the target policy. Similarly, for GANs, the policy guidance could be incorporated into the training process to steer the generator towards producing samples that are more in line with the target policy. The theoretical underpinnings would need to consider the specific architecture and training dynamics of VAEs and GANs to ensure the effective integration of policy guidance while maintaining the desired properties of the behavior-regularized target distribution.