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Trustworthy navigation with variational policy in deep reinforcement learning

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This framework enhances robust navigation and mapping in mobile robots using variational policy, indicating safer operations.

Trust-Nav enhances navigation by incorporating variational policy learning to quantify uncertainty in robot actions, ensuring safer navigation.
Experiments show that Trust-Nav outperforms deterministic deep reinforcement learning methods in noisy and complex environments.
The framework combines policy-based and value-based learning to optimize robot actions in unknown settings, maximizing cumulative rewards.
This approach signifies a significant advancement toward deployable robotic systems that can recognize and address their limitations.

Bockrath et al. (2025) studied this question.

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