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Found 3 papers, 1 papers with code
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
The first-stage network learns feature representations of the environment using low-level LiDAR statistics and the second-stage network combines those learned features with kinematics data.
Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution
Recently, reinforcement learning with deep neural networks has achieved great success in challenging continuous control problems such as 3D locomotion and robotic manipulation.
Seeing 3D Chairs: Exemplar Part-based 2D-3D Alignment using a Large Dataset of CAD Models
This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online.
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