Search Results for author:
Found 8 papers, 2 papers with code
Human Motion Prediction under Unexpected Perturbation
We investigate a new task in human motion prediction, which is predicting motions under unexpected physical perturbation potentially involving multiple people.
Reward Function Design for Crowd Simulation via Reinforcement Learning
Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner.
UGAE: A Novel Approach to Non-exponential Discounting
We also show experimentally that agents with non-exponential discounting trained via UGAE outperform variants trained with Monte Carlo advantage estimation.
Understanding reinforcement learned crowds
Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.
A Survey on Reinforcement Learning Methods in Character Animation
Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment.
A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism.
Data-Driven Crowd Simulation with Generative Adversarial Networks
This paper presents a novel data-driven crowd simulation method that can mimic the observed traffic of pedestrians in a given environment.
Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models
We automatically compute the optimal parameters for each of these different models based on prior tracked data and use the best model as motion prior for our particle-filter based tracking algorithm.
