Related papers: The Emergence of Individuality

The Emergence of Individuality

URL: http://arxiv.org/abs/2006.05842v2
Date: Mon, 18 Oct 2021 08:12:57 GMT
Title: The Emergence of Individuality
Authors: Jiechuan Jiang and Zongqing Lu
Abstract summary: We propose a simple yet efficient method for the emergence of individuality (EOI) in multi-agent reinforcement learning (MARL) EOI learns a probabilistic classifier that predicts a probability distribution over agents given their observation. We show that EOI significantly outperforms existing methods in a variety of multi-agent cooperative scenarios.
Score: 33.4713690991284
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Individuality is essential in human society, which induces the division of labor and thus improves the efficiency and productivity. Similarly, it should also be the key to multi-agent cooperation. Inspired by that individuality is of being an individual separate from others, we propose a simple yet efficient method for the emergence of individuality (EOI) in multi-agent reinforcement learning (MARL). EOI learns a probabilistic classifier that predicts a probability distribution over agents given their observation and gives each agent an intrinsic reward of being correctly predicted by the classifier. The intrinsic reward encourages the agents to visit their own familiar observations, and learning the classifier by such observations makes the intrinsic reward signals stronger and the agents more identifiable. To further enhance the intrinsic reward and promote the emergence of individuality, two regularizers are proposed to increase the discriminability of the classifier. We implement EOI on top of popular MARL algorithms. Empirically, we show that EOI significantly outperforms existing methods in a variety of multi-agent cooperative scenarios.

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