Genes in Intelligent Agents
- URL: http://arxiv.org/abs/2306.10225v2
- Date: Fri, 27 Oct 2023 14:10:23 GMT
- Title: Genes in Intelligent Agents
- Authors: Fu Feng, Jing Wang, Xu Yang and Xin Geng
- Abstract summary: Animals are born with some intelligence encoded in their genes, but machines lack such intelligence and learn from scratch.
Inspired by the genes of animals, we define the genes'' of machines named as the learngenes'' and propose the Genetic Reinforcement Learning (GRL)
GRL is a computational framework that simulates the evolution of organisms in reinforcement learning (RL) and leverages the learngenes to learn and evolve the intelligence agents.
- Score: 45.93363823594323
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The genes in nature give the lives on earth the current biological
intelligence through transmission and accumulation over billions of years.
Inspired by the biological intelligence, artificial intelligence (AI) has
devoted to building the machine intelligence. Although it has achieved thriving
successes, the machine intelligence still lags far behind the biological
intelligence. The reason may lie in that animals are born with some
intelligence encoded in their genes, but machines lack such intelligence and
learn from scratch. Inspired by the genes of animals, we define the ``genes''
of machines named as the ``learngenes'' and propose the Genetic Reinforcement
Learning (GRL). GRL is a computational framework that simulates the evolution
of organisms in reinforcement learning (RL) and leverages the learngenes to
learn and evolve the intelligence agents. Leveraging GRL, we first show that
the learngenes take the form of the fragments of the agents' neural networks
and can be inherited across generations. Second, we validate that the
learngenes can transfer ancestral experience to the agents and bring them
instincts and strong learning abilities. Third, we justify the Lamarckian
inheritance of the intelligent agents and the continuous evolution of the
learngenes. Overall, the learngenes have taken the machine intelligence one
more step toward the biological intelligence.
Related papers
- Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence [0.0]
The pursuit of creating artificial intelligence mirrors our longstanding fascination with understanding our own intelligence.
Recent advances in AI hold promise, but singular approaches often fall short in capturing the essence of intelligence.
This paper explores how fundamental principles from biological computation can guide the design of truly intelligent systems.
arXiv Detail & Related papers (2024-11-22T02:55:39Z) - Transferring Core Knowledge via Learngenes [45.651726289932334]
We propose the Genetic Transfer Learning (GTL) framework to copy the evolutionary process of organisms into neural networks.
GTL trains a population of networks, selects superior learngenes by tournaments, performs learngene mutations, and passes the learngenes to next generations.
We show that the learngenes bring the descendant networks instincts and strong learning ability.
arXiv Detail & Related papers (2024-01-16T06:18:11Z) - The Generative AI Paradox: "What It Can Create, It May Not Understand" [81.89252713236746]
Recent wave of generative AI has sparked excitement and concern over potentially superhuman levels of artificial intelligence.
At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans.
This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make?
arXiv Detail & Related papers (2023-10-31T18:07:07Z) - Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot
Evolution Better [2.884244918665901]
We investigate the question What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?''
Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not.
arXiv Detail & Related papers (2023-09-22T15:29:15Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - The Nature of Intelligence [0.0]
The essence of intelligence commonly represented by both humans and AI is unknown.
We show that the nature of intelligence is a series of mathematically functional processes that minimize system entropy.
This essay should be a starting point for a deeper understanding of the universe and us as human beings.
arXiv Detail & Related papers (2023-07-20T23:11:59Z) - Embodied Intelligence via Learning and Evolution [92.26791530545479]
We show that environmental complexity fosters the evolution of morphological intelligence.
We also show that evolution rapidly selects morphologies that learn faster.
Our experiments suggest a mechanistic basis for both the Baldwin effect and the emergence of morphological intelligence.
arXiv Detail & Related papers (2021-02-03T18:58:31Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Mimicking Evolution with Reinforcement Learning [10.35437633064506]
We argue that the path to developing artificial human-like-intelligence will pass through mimicking the evolutionary process in a nature-like simulation.
This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness.
arXiv Detail & Related papers (2020-03-31T18:16:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.