Jakob N. Foerster | DeepAI

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∙ 12/14/2022

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

The availability of challenging benchmarks has played a key role in the ...

0 Benjamin Ellis, et al. ∙

∙ 10/28/2022

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

Multiagent learning settings are inherently more difficult than single-a...

0 Dong-Ki Kim, et al. ∙

∙ 10/11/2022

Learning to Optimize Quasi-Newton Methods

We introduce a novel machine learning optimizer called LODO, which onlin...

0 Isaac Liao, et al. ∙

∙ 07/20/2022

Illusionary Attacks on Sequential Decision Makers and Countermeasures

Autonomous intelligent agents deployed to the real-world need to be robu...

5 Tim Franzmeyer, et al. ∙

∙ 07/14/2022

K-level Reasoning for Zero-Shot Coordination in Hanabi

The standard problem setting in cooperative multi-agent settings is self...

0 Brandon Cui, et al. ∙

∙ 03/07/2022

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

The main challenge of multiagent reinforcement learning is the difficult...

0 Dong-Ki Kim, et al. ∙

∙ 02/11/2020

Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization

Quantum hardware and quantum-inspired algorithms are becoming increasing...

23 Dmitrii Beloborodov, et al. ∙

∙ 12/04/2019

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

In recent years we have seen fast progress on a number of benchmark prob...

17 Hengyuan Hu, et al. ∙

∙ 10/23/2019

Robust Domain Randomization for Reinforcement Learning

Producing agents that can generalize to a wide range of environments is ...

21 Reda Bahi Slaoui, et al. ∙

∙ 09/09/2019

Exploratory Combinatorial Optimization with Reinforcement Learning

Many real-world problems can be reduced to combinatorial optimization on...

35 Thomas D. Barrett, et al. ∙

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

54 Nolan Bard, et al. ∙

∙ 11/04/2018

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

When observing the actions of others, humans carry out inferences about ...

0 Jakob N. Foerster, et al. ∙

∙ 10/27/2018

Multi-Agent Common Knowledge Reinforcement Learning

In multi-agent reinforcement learning, centralised policies can only be ...

0 Jakob N. Foerster, et al. ∙

∙ 09/13/2017

Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learnin...

0 Jakob N. Foerster, et al. ∙

∙ 11/28/2016

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

There exist many problem domains where the interpretability of neural ne...

0 Jakob N. Foerster, et al. ∙

∙ 02/08/2016

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

We propose deep distributed recurrent Q-networks (DDRQN), which enable t...

0 Jakob N. Foerster, et al. ∙

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