Search Results for author:
Found 3 papers, 1 papers with code
Human-compatible driving partners through data-regularized self-play reinforcement learning
Therefore, incorporating realistic human agents is essential for scalable training and evaluation of autonomous driving systems in simulation.
Using Cooperative Game Theory to Prune Neural Networks
We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks.
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
