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08/07/2023
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
StarCraft II is one of the most challenging simulated reinforcement lear...
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05/17/2023
Exploring the Space of Key-Value-Query Models with Intention
Attention-based models have been a key element of many recent breakthrou...
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06/21/2022
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
Real-world competitive games, such as chess, go, or StarCraft II, rely o...
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10/08/2021
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Strategic diversity is often essential in games: in multi-player games, ...
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07/27/2021
Open-Ended Learning Leads to Generally Capable Agents
In this work we create agents that can perform well beyond a single, ind...
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10/27/2020
Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
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04/20/2020
Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
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02/14/2020
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Adversarial training, a special case of multi-objective optimization, is...
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12/16/2019
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Sko...
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02/06/2019
Distilling Policy Distillation
The transfer of knowledge from one policy to another is an important too...
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06/05/2018
Mix&Match - Agent Curricula for Reinforcement Learning
We introduce Mix&Match (M&M) - a training framework designed to facilita...
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03/10/2018
Kickstarting Deep Reinforcement Learning
We present a method for using previously-trained 'teacher' agents to kic...
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11/27/2017
Population Based Training of Neural Networks
Neural networks dominate the modern machine learning landscape, but thei...
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07/13/2017
Distral: Robust Multitask Reinforcement Learning
Most deep reinforcement learning algorithms are data inefficient in comp...
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06/20/2017
Grounded Language Learning in a Simulated 3D World
We are increasingly surrounded by artificially intelligent technology th...
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06/16/2017
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning w...
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03/01/2017
Understanding Synthetic Gradients and Decoupled Neural Interfaces
When training neural networks, the use of Synthetic Gradients (SG) allow...
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02/07/2017
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Maybe the single most important goal of representation learning is makin...
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11/19/2016
Local minima in training of neural networks
There has been a lot of recent interest in trying to characterize the er...
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11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
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08/18/2016
Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forward-propagating...
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02/19/2016
Learning to SMILE(S)
This paper shows how one can directly apply natural language processing ...
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04/18/2015
On the consistency of Multithreshold Entropy Linear Classifier
Multithreshold Entropy Linear Classifier (MELC) is a recent classifier i...
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04/18/2015
Fast optimization of Multithreshold Entropy Linear Classifier
Multithreshold Entropy Linear Classifier (MELC) is a density based model...
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08/04/2014
