142 citations · 299 across the 8 of their papers we have counts for
5 papers · 1 filter
Malthusian Reinforcement Learning
Joel Z. Leibo, Julien Perolat, Edward Hughes +6
Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked popu…
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…
A Generalised Method for Empirical Game Theoretic Analysis
Karl Tuyls, Julien Perolat, Marc Lanctot +2
This paper provides theoretical bounds for empirical game theoretical analysis of complex multi-agent interactions. We provide insights in the empirical meta game showing that a Na…