1.1k citations · 3k across the 23 of their papers we have counts for
9 papers · 1 filter
Improving Policies via Search in Cooperative Partially Observable Games
Adam Lerer, Hengyuan Hu, Jakob Foerster +1
Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just…
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Hengyuan Hu, Jakob N Foerster
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common…
Learning with Opponent-Learning Awareness
Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat +3
Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be exten…
Fake News in Social Networks
Christoph Aymanns, Jakob Foerster, Co-Pierre Georg +1
We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in un…
Counterfactual Multi-Agent Policy Gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras +2
Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a grea…
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar +4
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing mul…
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Jakob N. Foerster, Justin Gilmer, Jan Chorowski +2
There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switc…
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas +1
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communicati…
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas +1
We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. In these tasks, the agents are not…