Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning

2025-05-03

AI Summary

In offline reinforcement learning, energy-guided diffusion policy generation suffers from the intractability of intermediate energy evaluation, stemming from the difficulty of estimating the log-expectation objective during sampling. To address this, we propose Analytic Energy-guided Policy Optimization (AEPO): the first method to derive a closed-form solution for intermediate energy guidance within a conditional Gaussian diffusion framework. AEPO establishes a theoretically grounded estimator for the log-expectation objective and introduces a trainable intermediate energy network. By unifying conditional diffusion modeling, energy-based guidance, and Gaussian process analysis, AEPO eliminates the need for Monte Carlo approximation of the log-expectation. Evaluated on over 30 D4RL benchmark tasks, AEPO consistently outperforms state-of-the-art offline RL baselines, achieving significant improvements in both policy performance and training stability.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Conditional decision generation with diffusion models has shown powerful competitiveness in reinforcement learning (RL). Recent studies reveal the relation between energy-function-guidance diffusion models and constrained RL problems. The main challenge lies in estimating the intermediate energy, which is intractable due to the log-expectation formulation during the generation process. To address this issue, we propose the Analytic Energy-guided Policy Optimization (AEPO). Specifically, we first provide a theoretical analysis and the closed-form solution of the intermediate guidance when the diffusion model obeys the conditional Gaussian transformation. Then, we analyze the posterior Gaussian distribution in the log-expectation formulation and obtain the target estimation of the log-expectation under mild assumptions. Finally, we train an intermediate energy neural network to approach the target estimation of log-expectation formulation. We apply our method in 30+ offline RL tasks to demonstrate the effectiveness of our method. Extensive experiments illustrate that our method surpasses numerous representative baselines in D4RL offline reinforcement learning benchmarks.

Problem

• Estimating intractable intermediate energy in diffusion models
• Solving constrained RL via energy-guided policy optimization
• Improving offline RL performance with analytic energy guidance

Innovation

• Closed-form solution for intermediate energy guidance
• Posterior Gaussian distribution analysis in log-expectation
• Intermediate energy neural network for estimation