The Evolution theory of Learning: From Natural Selection to Reinforcement Learning

06/16/2023
by   Taboubi Ahmed, et al.
0

Evolution is a fundamental process that shapes the biological world we inhabit, and reinforcement learning is a powerful tool used in artificial intelligence to develop intelligent agents that learn from their environment. In recent years, researchers have explored the connections between these two seemingly distinct fields, and have found compelling evidence that they are more closely related than previously thought. This paper examines these connections and their implications, highlighting the potential for reinforcement learning principles to enhance our understanding of evolution and the role of feedback in evolutionary systems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/26/2020

Lineage Evolution Reinforcement Learning

We propose a general agent population learning system, and on this basis...
research
10/15/2019

How a minimal learning agent can infer the existence of unobserved variables in a complex environment

According to a mainstream position in contemporary cognitive science and...
research
02/03/2022

Reward is not enough: can we liberate AI from the reinforcement learning paradigm?

I present arguments against the hypothesis put forward by Silver, Singh,...
research
03/26/2015

An Evolutionary Algorithm for Error-Driven Learning via Reinforcement

Although different learning systems are coordinated to afford complex be...
research
06/20/2023

Coevolution of cognition and cooperation in structured populations under reinforcement learning

We study the evolution of behavior under reinforcement learning in a Pri...
research
07/17/2019

Photonic architecture for reinforcement learning

The last decade has seen an unprecedented growth in artificial intellige...
research
10/22/2013

Evolution of swarming behavior is shaped by how predators attack

Animal grouping behaviors have been widely studied due to their implicat...

Please sign up or login with your details

Forgot password? Click here to reset