Audrūnas Gruslys | DeepAI

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∙ 06/30/2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

We introduce DeepNash, an autonomous agent capable of learning to play t...

6 Julien Perolat, et al. ∙

∙ 08/27/2020

The Advantage Regret-Matching Actor-Critic

Regret minimization has played a key role in online learning, equilibriu...

0 Audrūnas Gruslys, et al. ∙

∙ 05/04/2020

Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI

Multiplayer games have a long history in being used as key testbeds for ...

11 Shayegan Omidshafiei, et al. ∙

∙ 06/01/2019

Neural Replicator Dynamics

In multiagent learning, agents interact in inherently nonstationary envi...

12 Shayegan Omidshafiei, et al. ∙

∙ 01/24/2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

Psychlab is a simulated psychology laboratory inside the first-person 3D...

0 Joel Z. Leibo, et al. ∙

∙ 11/02/2017

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with ...

0 Marc Lanctot, 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. ∙

∙ 04/15/2017

The Reactor: A Sample-Efficient Actor-Critic Architecture

In this work we present a new reinforcement learning agent, called React...

0 Audrūnas Gruslys, et al. ∙

∙ 04/12/2017

Deep Q-learning from Demonstrations

Deep reinforcement learning (RL) has achieved several high profile succe...

0 Todd Hester, et al. ∙

∙ 06/10/2016

Memory-Efficient Backpropagation Through Time

We propose a novel approach to reduce memory consumption of the backprop...

0 Audrūnas Gruslys, et al. ∙

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