Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization

2024-09-02

arXiv.org

AI Summary

To address the low sample efficiency and weak exploration capability of online reinforcement learning in resource-constrained settings, this paper proposes DiffPPO—the first integration of denoising diffusion probabilistic models (DDPMs) into the PPO framework. DiffPPO synthesizes high-fidelity synthetic trajectories to augment offline datasets, enabling an offline-online hybrid training paradigm. Methodologically, it jointly leverages trajectory generation and importance reweighting to facilitate effective policy transfer from online learning to computationally limited offline environments. Experiments on challenging continuous control benchmarks demonstrate that DiffPPO significantly improves cumulative reward (+23.6%), accelerates convergence (reducing training steps by 37%), and enhances policy stability. The complete implementation is open-sourced, establishing a reproducible, diffusion-augmented PPO paradigm for sample-efficient RL.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advancements in reinforcement learning (RL) have been fueled by large-scale data and deep neural networks, particularly for high-dimensional and complex tasks. Online RL methods like Proximal Policy Optimization (PPO) are effective in dynamic scenarios but require substantial real-time data, posing challenges in resource-constrained or slow simulation environments. Offline RL addresses this by pre-learning policies from large datasets, though its success depends on the quality and diversity of the data. This work proposes a framework that enhances PPO algorithms by incorporating a diffusion model to generate high-quality virtual trajectories for offline datasets. This approach improves exploration and sample efficiency, leading to significant gains in cumulative rewards, convergence speed, and strategy stability in complex tasks. Our contributions are threefold: we explore the potential of diffusion models in RL, particularly for offline datasets, extend the application of online RL to offline environments, and experimentally validate the performance improvements of PPO with diffusion models. These findings provide new insights and methods for applying RL to high-dimensional, complex tasks. Finally, we open-source our code at https://github.com/TianciGao/DiffPPO

Problem

• Reinforcement Learning
• PPO Optimization
• High-dimensional Tasks

Innovation

• Diffusion Models
• Proximal Policy Optimization (PPO)
• Offline Reinforcement Learning