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We use a stack-augmented recurrent neural network (stack-RNN) as the generative model trained with cross-entropy loss function minimization and the REINFORCE algorithm to conduct policy gradient updates during learning110,111. The stack-RNN architecture is particularly suited for sequence prediction ...
machine-learninginverse-reinforcement-learningmaximum-entropy UpdatedApr 21, 2024 Jupyter Notebook [T-ITS] Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning autonomous-drivinginverse-reinforcement-learning ...
Both networks are trained using the Binary Cross-Entropy loss function (PyTorch 2.3.0 BCELoss function). The training process employs the Adam optimizer with a learning rate of 0.0001, a batch size of 16, and runs for 800 epochs. The entire training dataset (N = 8500 samples) is ...
This paper considers the Inverse Reinforcement Learning (IRL) problem, that is inferring a reward function for which a demonstrated expert policy is optimal. We propose to break the IRL problem down into two generic Supervised Learning steps: this is the Cascaded Supervised IRL (CSI) approach. A...
(2018) use maximum entropy IRL to learn a reward function for following a target as opposed to avoiding pedestrians. Haptic assistance provides feedback to a human controlling a system through the control interface in the form of torque or force. This concept is especially useful for tele...
This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore. WildLight: In-the-wild Inverse Rendering...
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