A PPO-based multi-agent CNN policy that jointly controls movement, existence detection, and reachability detection, with a transfer-learned estimator for unreachable targets, outperforms three baselines in simulated radiation localization.
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Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
A PPO-based multi-agent CNN policy that jointly controls movement, existence detection, and reachability detection, with a transfer-learned estimator for unreachable targets, outperforms three baselines in simulated radiation localization.