  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.
