Training intelligent agents in Reinforcement Learning (RL) is much more time-consuming than animal learning. This is because agents learn from scratch, but animals learn with genes inherited from ancestors and are born with some innate abilities. Inspired by genes in animals, here we conceptualize the gene in intelligent agents and introduce Genetic Reinforcement Learning (GRL), a computational framework to represent, evaluate, and evolve genes (in agents). Leveraging GRL we identify genes and demonstrate several advantages of genes. First, we find that genes take the form of the fragment of agents' neural networks and can be inherited across generations. Second, we validate that genes bring better and stabler learning ability to agents, since genes condense knowledge from ancestors and bring agent with innate abilities. Third, we present evidence of Lamarckian evolution in intelligent agents. The continuous encoding of knowledge into genes across generations facilitates the evolution of genes. Overall, our work promotes a novel paradigm to train agents by incorporating genes.

翻译：在强化学习中训练智能体比动物学习耗时得多。这是因为智能体从零开始学习，而动物则通过继承祖先的基因，天生具备某些本能能力。受动物基因启发，本文提出了智能体中的基因概念，并引入遗传强化学习（GRL）这一计算框架，用于表示、评估和演化（智能体中的）基因。利用GRL，我们识别出基因并证明了其多项优势。首先，我们发现基因表现为智能体神经网络的片段形式，并能够跨代遗传。其次，我们验证了基因能够为智能体带来更优且更稳定的学习能力，因为基因浓缩了祖先的知识，赋予智能体先天能力。第三，我们展示了智能体中存在拉马克式演化的证据。通过跨代持续将知识编码为基因，促进了基因的演化。总体而言，本研究提出了一种通过整合基因来训练智能体的新范式。