1.2k citations · 4.4k across the 42 of their papers we have counts for
8 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…
Relational Forward Models for Multi-Agent Learning
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano +5
The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to…
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…
Adaptive Mechanism Design: Learning to Promote Cooperation
Tobias Baumann, Thore Graepel, John Shawe-Taylor
In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of…
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…