Bibliographic Information: Chen, T., Wang, Z., & Zhou, M. (2024). Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning. Advances in Neural Information Processing Systems, 38.
Research Objective: This paper introduces Diffusion Trusted Q-Learning (DTQL), a novel offline reinforcement learning algorithm that addresses the computational challenges of diffusion-based methods while maintaining their expressiveness for improved performance.
Methodology: DTQL employs a dual-policy approach: a diffusion policy for behavior cloning and a one-step policy for action generation. A novel diffusion trust region loss constrains the one-step policy within the high-density regions of the data manifold defined by the diffusion policy, ensuring safe exploration. The one-step policy is further optimized by maximizing the Q-value function for reward maximization. The algorithm is evaluated on the D4RL benchmark and compared against state-of-the-art offline RL methods.
Key Findings: DTQL achieves state-of-the-art results on the majority of D4RL benchmark tasks, outperforming both conventional and other diffusion-based offline RL methods. It demonstrates significant improvements in training and inference time efficiency compared to existing diffusion-based methods, primarily due to the elimination of iterative denoising sampling during both training and inference.
Main Conclusions: DTQL offers a computationally efficient and highly effective approach for offline reinforcement learning by combining the strengths of diffusion models with a novel trust region loss and a dual-policy framework.
Significance: This research contributes to the advancement of offline RL by addressing the limitations of existing diffusion-based methods, paving the way for more efficient and practical applications of these powerful techniques.
Limitations and Future Research: While DTQL shows promising results, further exploration of one-step policy design and potential improvements in benchmark performance are warranted. Future research could investigate its application in online settings and with more complex input data, such as images. Additionally, incorporating distributional reinforcement learning principles for reward estimation could be beneficial.
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by Tianyu Chen,... في arxiv.org 11-04-2024
https://arxiv.org/pdf/2405.19690.pdfاستفسارات أعمق