Sep 28, 2018 · Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' ...
The behavioral dynamics of multi-agent systems have a rich and orderly struc- ture, which can be leveraged to understand these systems, and to improve how.
RFMs are based on Graph Networks [Battaglia et al., 2018] (GN). RFM takes a semantic graph as an input and outputs either an action prediction for agents.
Dec 20, 2018 · Relational Forward Models for multi-agent learning make accurate predictions of agents' future behavior, they produce intepretable ...
Sep 28, 2018 · The behavioral dynamics of multi-agent systems have a rich and orderly struc- ture, which can be leveraged to understand these systems, ...
Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in ...
We use three architectures: two ubiquitous ones (a convolutional neural network (CNN) and a multi-layer perceptron (MLP)), and a Relational Forward Model (RFM) ...
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning · Relational Forward Models for Multi-Agent Learning · M^3RL: Mind-aware Multi-agent ...
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This paper discovers that NRI can be fundamentally limited without sufficient long-term observations, and proposes an extension of NRI, which is called the ...
Sep 28, 2018 · Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' ...