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Variational OOD State Correction for Offline Reinforcement Learning

Created by
  • Haebom

Author

Ke Jiang, Wen Jiang, Xiaoyang Tan

Outline

In this paper, we propose a novel method, Density-Aware Safety Perception (DASP), to solve the state distribution shift problem in offline reinforcement learning. DASP encourages agents to prioritize actions that lead to outcomes with high data density, and to return to or within the (safe) region of the distribution. To this end, we optimize the objective function within a variational framework that simultaneously considers the potential outcomes of a decision and their density, providing important context information for safe decision making. We verify the effectiveness and feasibility of the proposed method through extensive experiments in MuJoCo and AntMaze offline environments.

Takeaways, Limitations

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Takeaways:
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A novel approach to solving the state distribution shift problem in offline reinforcement learning
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Provides contextual information for safe decision making, taking into account data density
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Validation of effectiveness and feasibility by MuJoCo and AntMaze
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Limitations:
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Further research is needed on the generalization performance of the proposed method.
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Need to evaluate applicability to various environments and complex problems
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The need to tune optimization parameters for specific problem domains
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