DenseMem Cross Level Credit Assignment over Hierarchical Memory for Long Horizon Agentic Decision Making
This paper introduces DenseMem, a method combining bounded episodic memory with signed cross-level credit allocation to improve delayed-reward learning, but experimental results indicate that while it outperforms flat credit assignment in some tasks, it does not consistently surpass unshaped learning or improve route efficiency, ultimately offering an auditable allocation mechanism with identified limitations rather than proof of general superiority for long-horizon decision making.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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