Cost-Aware Diffusion Active Search
Arundhati Banerjee, Jeff Schneider

TL;DR
This paper introduces a diffusion model-based approach for active search that balances exploration and exploitation efficiently, outperforming traditional methods in recovery rate and computational cost.
Contribution
It leverages diffusion models for lookahead action sampling in active search, reducing computational complexity compared to tree search methods.
Findings
Outperforms baselines in offline reinforcement learning
Achieves higher full recovery rate
More computationally efficient than tree search
Abstract
Active search for recovering objects of interest through online, adaptive decision making with autonomous agents requires trading off exploration of unknown environments with exploitation of prior observations in the search space. Prior work has proposed information gain and Thompson sampling based myopic, greedy approaches for agents to actively decide query or search locations when the number of targets is unknown. Decision making algorithms in such partially observable environments have also shown that agents capable of lookahead over a finite horizon outperform myopic policies for active search. Unfortunately, lookahead algorithms typically rely on building a computationally expensive search tree that is simulated and updated based on the agent's observations and a model of the environment dynamics. Instead, in this work, we leverage the sequence modeling abilities of diffusion…
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Taxonomy
TopicsDiffusion and Search Dynamics · Optimization and Search Problems · Reinforcement Learning in Robotics
