通向通用AI的生态系统路径
The Ecosystem Path to General AI
摘要：
我们首先讨论生态系统模拟器和通用AI之间的联系。然后，我们介绍了开源生态系统模拟器Ecotwin，它基于游戏引擎Unity，在包含山脉和湖泊等无生命物体以及动物和植物等生物体的生态系统上运行。动物认知是通过整合三个独立的网络来建模的：（i）用于硬连线反射的反射网络;（ii）将氧气，水，能量和气味等感官数据映射到标量幸福值的幸福网络;以及（iii）用于选择行动的策略网络。策略网络采用强化学习（RL）进行训练，其中奖励信号被定义为从一个时间步到下一个时间步的幸福差异。所有的生物体都能够进行有性或无性繁殖，如果它们耗尽了关键的资源，它们就会死亡。我们报告了三项与Ecotwin的研究结果，其中自然现象出现在模型中，而不是硬连线。首先，我们研究了一个陆地生态系统与狼，鹿，草，其中出现了洛特卡-沃尔泰拉风格的人口动态。其次，我们研究了一个海洋生态系统中的浮游植物，桡足类和磷虾，其中的昼夜垂直迁移行为出现。第三，我们研究了一个涉及致命危险的生态系统，其中某些将RL与反射相结合的代理优于纯RL代理。
英文摘要：
We start by discussing the link between ecosystem simulators and general AI. Then we present the open-source ecosystem simulator Ecotwin, which is based on the game engine Unity and operates on ecosystems containing inanimate objects like mountains and lakes, as well as organisms such as animals and plants. Animal cognition is modeled by integrating three separate networks: (i) a reflex network for hard-wired reflexes; (ii) a happiness network that maps sensory data such as oxygen, water, energy, and smells, to a scalar happiness value; and (iii) a policy network for selecting actions. The policy network is trained with reinforcement learning (RL), where the reward signal is defined as the happiness difference from one time step to the next. All organisms are capable of either sexual or asexual reproduction, and they die if they run out of critical resources. We report results from three studies with Ecotwin, in which natural phenomena emerge in the models without being hardwired. First, we study a terrestrial ecosystem with wolves, deer, and grass, in which a Lotka-Volterra style population dynamics emerges. Second, we study a marine ecosystem with phytoplankton, copepods, and krill, in which a diel vertical migration behavior emerges. Third, we study an ecosystem involving lethal dangers, in which certain agents that combine RL with reflexes outperform pure RL agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
