153 citations · 457 across the 11 of their papers we have counts for
3 papers · 2 filters
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…
Playing the Game of Universal Adversarial Perturbations
Julien Perolat, Mateusz Malinowski, Bilal Piot +1
We study the problem of learning classifiers robust to universal adversarial perturbations. While prior work approaches this problem via robust optimization, adversarial training,…
Re-evaluating Evaluation
David Balduzzi, Karl Tuyls, Julien Perolat +1
Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversar…