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Found 5 papers, 1 papers with code
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error
We propose a novel method that introduces a weighting factor for each experience when calculating the loss function at the learning stage.
A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective
In this paper, a domain adaptive training framework for absolute pose regression is introduced.
Sensing accident-prone features in urban scenes for proactive driving and accident prevention
To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam.
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training
For proving texture invariance, we investigate the generalization of the proposed method augmenting the datasets to different scene styles, as ablation studies, using generative adversarial networks.
Two-stage training algorithm for AI robot soccer
Because these two training processes are conducted in a series in every timestep, agents can learn how to maximize role rewards and team rewards simultaneously.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
