180 citations · 1.5k across the 49 of their papers we have counts for
10 papers · 1 filter
A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland +12
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1)…
Multiagent Evaluation under Incomplete Information
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls +4
This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Tradit…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
Neural Replicator Dynamics
Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei +8
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environmen…
Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sebastien Racaniere +4
Deep learning is built on the foundational guarantee that gradient descent on an objective function converges to local minima. Unfortunately, this guarantee fails in settings, such…
Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
James Butterworth, Rahul Savani, Karl Tuyls
Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are an alternative that use…