Evolving Self-supervised Neural Networks: Autonomous Intelligence from Evolved Self-teaching

Understand

This paper presents a technique called evolving self-supervised neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward.

• The proposed method presents some sort-of paradigm shift, and differs greatly from both traditional gradient-based learning and evolutionary algorithms in that it combines the metaphor of evolution and learning, more specifically self-learning, together, rather than treating these phenomena alternatively.
• I simulate a multi-agent system in which neural networks are used to control autonomous foraging agents with little domain knowledge.
• Experimental results show that only evolved self-supervised agents can demonstrate some sort of intelligent behaviour, but not evolution or self-learning alone.