Search Results for author: Guy Lever

Found 13 papers, 4 papers with code

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

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 simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning Reinforcement Learning (RL)

From Motor Control to Team Play in Simulated Humanoid Football

1 code implementation25 May 2021 SiQi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.

Imitation Learning Multi-agent Reinforcement Learning +1

Biases for Emergent Communication in Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.

Multi-agent Reinforcement Learning reinforcement-learning +1

Emergent Coordination Through Competition

no code implementations ICLR 2019 Si-Qi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.

Continuous Control Reinforcement Learning (RL)

Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent

no code implementations7 Jul 2016 Aleksandar Botev, Guy Lever, David Barber

We present a unifying framework for adapting the update direction in gradient-based iterative optimization methods.


A Gauss-Newton Method for Markov Decision Processes

no code implementations29 Jul 2015 Thomas Furmston, Guy Lever

In this work we investigate approximate Newton methods for policy optimization in Markov Decision Processes (MDPs).

Modelling transition dynamics in MDPs with RKHS embeddings

no code implementations18 Jun 2012 Steffen Grunewalder, Guy Lever, Luca Baldassarre, Massi Pontil, Arthur Gretton

For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation.

Online Prediction on Large Diameter Graphs

no code implementations NeurIPS 2008 Mark Herbster, Guy Lever, Massimiliano Pontil

Current on-line learning algorithms for predicting the labelling of a graph have an important limitation in the case of large diameter graphs; the number of mistakes made by such algorithms may be proportional to the square root of the number of vertices, even when tackling simple problems.

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