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Poster
Biases for Emergent Communication in Multi-agent Reinforcement Learning
Tom Eccles · Yoram Bachrach · Guy Lever · Angeliki Lazaridou · Thore Graepel

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #195
in Reinforcement Learning and Planning -- Multi-Agent RL »

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended reinforcement learning domains, it has proved hard to learn such communication without centralized training of agents, due in part to a difficult joint exploration problem. We introduce inductive biases for positive signalling and positive listening, which ease this problem. In a simple one-step environment, we demonstrate how these biases ease the learning problem. We also apply our methods to a more extended environment, showing that agents with these inductive biases achieve better performance, and analyse the resulting communications protocols.

Keywords: Multi-Agent RL · Reinforcement Learning and Planning
[ Paper]  [ Poster] 

Author Information

Tom Eccles (DeepMind)
Yoram Bachrach (Google DeepMind)
Guy Lever (Google DeepMind)
Angeliki Lazaridou (DeepMind)
Thore Graepel (DeepMind)

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