Wojciech Marian Czarnecki | DeepAI

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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...

0 Michael Mathieu, et al. ∙

∙ 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...

0 Marta Garnelo, et al. ∙

∙ 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...

18 Quentin Bertrand, et al. ∙

∙ 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, ...

0 Marta Garnelo, et al. ∙

∙ 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...

2 Open-Ended Learning Team, et al. ∙

∙ 10/27/2020

Behavior Priors for Efficient Reinforcement Learning

As we deploy reinforcement learning agents to solve increasingly challen...

10 Dhruva Tirumala, et al. ∙

∙ 04/20/2020

Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (...

10 Wojciech Marian Czarnecki, et al. ∙

∙ 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...

0 Gauthier Gidel, et al. ∙

∙ 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...

32 Wojciech Marian Czarnecki, et al. ∙

∙ 02/06/2019

Distilling Policy Distillation

The transfer of knowledge from one policy to another is an important too...

14 Wojciech Marian Czarnecki, et al. ∙

∙ 06/05/2018

Mix&Match - Agent Curricula for Reinforcement Learning

We introduce Mix&Match (M&M) - a training framework designed to facilita...

2 Wojciech Marian Czarnecki, et al. ∙

∙ 03/10/2018

Kickstarting Deep Reinforcement Learning

We present a method for using previously-trained 'teacher' agents to kic...

0 Simon Schmitt, et al. ∙

∙ 11/27/2017

Population Based Training of Neural Networks

Neural networks dominate the modern machine learning landscape, but thei...

0 Max Jaderberg, et al. ∙

∙ 07/13/2017

Distral: Robust Multitask Reinforcement Learning

Most deep reinforcement learning algorithms are data inefficient in comp...

0 Yee Whye Teh, et al. ∙

∙ 06/20/2017

Grounded Language Learning in a Simulated 3D World

We are increasingly surrounded by artificially intelligent technology th...

0 Karl Moritz Hermann, et al. ∙

∙ 06/16/2017

Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning w...

0 Peter Sunehag, et al. ∙

∙ 03/01/2017

Understanding Synthetic Gradients and Decoupled Neural Interfaces

When training neural networks, the use of Synthetic Gradients (SG) allow...

0 Wojciech Marian Czarnecki, et al. ∙

∙ 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...

0 Stanisław Jastrzębski, et al. ∙

∙ 11/19/2016

Local minima in training of neural networks

There has been a lot of recent interest in trying to characterize the er...

0 Grzegorz Świrszcz, et al. ∙

∙ 11/16/2016

Reinforcement Learning with Unsupervised Auxiliary Tasks

Deep reinforcement learning agents have achieved state-of-the-art result...

0 Max Jaderberg, et al. ∙

∙ 08/18/2016

Decoupled Neural Interfaces using Synthetic Gradients

Training directed neural networks typically requires forward-propagating...

0 Max Jaderberg, et al. ∙

∙ 02/19/2016

Learning to SMILE(S)

This paper shows how one can directly apply natural language processing ...

0 Stanisław Jastrzębski, et al. ∙

∙ 04/18/2015

On the consistency of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier i...

0 Wojciech Marian Czarnecki, et al. ∙

∙ 04/18/2015

Fast optimization of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a density based model...

0 Rafal Jozefowicz, et al. ∙

∙ 08/04/2014

Multithreshold Entropy Linear Classifier

Linear classifiers separate the data with a hyperplane. In this paper we...

0 Wojciech Marian Czarnecki, et al. ∙

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