452 citations · 907 across the 16 of their papers we have counts for
17 papers
NfgTransformer: Equivariant Representation Learning for Normal-form Games
Siqi Liu, Luke Marris, Georgios Piliouras +2
Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- a…
Neural Population Learning beyond Symmetric Zero-sum Games
Siqi Liu, Luke Marris, Marc Lanctot +3
We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing metho…
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
Luke Marris, Ian Gemp, Thomas Anthony +3
Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunat…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Siqi Liu, Marc Lanctot, Luke Marris +1
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games. In this paper, we propose simplex-NeuPL th…
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan +6
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control…
NeuPL: Neural Population Learning
Siqi Liu, Luke Marris, Daniel Hennes +3
Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, gr…
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu +5
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of…
Launchpad: A Programming Model for Distributed Machine Learning Research
Fan Yang, Gabriel Barth-Maron, Piotr Stańczyk +5
A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems i…
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…
dm_control: Software and Tasks for Continuous Control
Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal +8
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides c…
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg +11
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy…
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)…
Emergent Coordination Through Competition
Siqi Liu, Guy Lever, Josh Merel +3
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated p…
Hierarchical visuomotor control of humanoids
Josh Merel, Arun Ahuja, Vu Pham +5
We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioc…
Observational Learning by Reinforcement Learning
Diana Borsa, Bilal Piot, Rémi Munos +1
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core me…
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Siqi Liu, Zhenhai Zhu, Ning Ye +2
Current image captioning methods are usually trained via (penalized) maximum likelihood estimation. However, the log-likelihood score of a caption does not correlate well with huma…