【摘要】 群体行为往往能产生远超个体行为的价值和复杂度。为了在个体智能的基础上更有效地衍生出群体智能,需要基于群体熵来科学地衡量群体智能水平,并以群体熵为引导目标,推动群体智能的增强和演进。针对这个重要的科学问题,以无人小车群体为研究对象,提出基于参数共享和群体策略熵的多智能体soft Q learning算法,通过共享智能体的观测信息,并结合最大熵强化学习方法,实现探索型任务中群体策略的持续学习更新。同时,通过将群体熵定义为度量工具,刻画群体学习中熵变化模式,实现对群智汇聚过程的定量分析。更多还原

【Abstract】 Swarm behavior can often produce value and complexity far beyond individual behavior. In order to more effectively derive swarm intelligence on the basis of individual intelligence, it is necessary to scientifically measure the level of swarm intelligence based on swarm entropy, and use swarm entropy as the guiding goal to promote the enhancement and evolution of swarm intelligence. Aiming at this important scientific problem, the unmanned car group as the research object was taken and a multi-agent soft Q learning method based on parameter sharing and group strategy entropy was proposed. Which by sharing the observation information of the agent, combined with the maximum entropy reinforcement learning method, to achieve continuous learning and updating of swarm strategies in exploratory tasks. At the same time, by defining swarm entropy as a measurement tool, characterizing the entropy change pattern in swarm learning, realizing the quantitative analysis of the gathering process of swarm intelligence.更多还原