The content introduces the MISA framework for offline reinforcement learning, emphasizing mutual information regularization. It addresses challenges of distributional shift in offline RL, proposing a novel approach to improve policy evaluation and policy improvement. The experiments demonstrate that MISA outperforms various baselines on D4RL benchmarks across different tasks.
The paper discusses the theoretical foundations of mutual information estimation in the context of RL and presents practical solutions for estimating mutual information lower bounds. It highlights the importance of accurate mutual information estimation in improving offline RL performance. The visualization results show how MISA effectively clusters state-action pairs based on rewards, demonstrating its robust representation learning capabilities.
Overall, the content provides a comprehensive overview of the MISA framework, its experimental evaluations, and comparisons with existing methods in offline reinforcement learning.
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by Xiao Ma,Bing... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2210.07484.pdfDeeper Inquiries