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Found 6 papers, 0 papers with code
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent.
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Generating a robust representation of the environment is a crucial ability of learning agents.
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Our filter module splits the filter task into multiple less complex and more interpretable subtasks.
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important.
Wasserstein Adversarial Imitation Learning
Imitation Learning describes the problem of recovering an expert policy from demonstrations.
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach.
