Search Results for author:
Found 5 papers, 1 papers with code
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.
Behaviour-conditioned policies for cooperative reinforcement learning tasks
We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner.
