In this article, I show the robustness of optimality of exploration ratioagainst the number of agents (agent population)under multiagent learning (MAL) situation in nonstationary environments.Agent population will affect efficiency of agents' learning becauseeach agent's learning causes noisy factors for others.From this point, exploration ratio should be small to make MAL effective.In nonstationary environments, on the other hand, each agent needs explore with enough probability to catch-upchanges of the environments.This means the exploration ratio need to be significantly large.I investigate the relation between the population and the efficiency ofexploration based on a theorem about relations betweenthe exploration ratio and a lower boundary of learning error.Finally, it is shown that the population of the agents does not affectthe optimal exploration ratio under a certain condition.This consequence is confirmed by several experimentsusing population games with various reward functions.
But, then again, the aging of populations will reduce the number of workers in developed and some developing countries by many millions. The dependency ratio -- the ratio of people of non-working age to those of working age -- is a crude measure of the scale of the problem. Globally, the dependency ratio of non-working older people to workers has indeed risen over the 56 years, from 8.6 percent to 13 percent, and will continue to increase. Even in Japan, with its low birth and immigration rates, and an old-dependency ratio that has gone up by a remarkable 35 percentage points, from 9 percent to 44 percent, the total ratio has risen by a much more modest 10 percentage points, to 66 percent.
Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence. In recent years, multiagent settings have become an effective platform for deep reinforcement learning research. Despite this progress, there are still two main challenges for multiagent reinforcement learning. We need to create open-ended tasks with a high complexity ceiling: current environments are either complex but too narrow or open-ended but too simple.
Males and females are ecologically distinct in many species, but whether responses to climate change are sex-specific is unknown. Increased elevation was associated with increased water availability and female frequency, likely owing to sex-specific water use efficiency and survival. Recent aridification caused male frequency to move upslope at 175 meters per decade, a rate of trait shift outpacing reported species' range shifts by an order of magnitude. This increase in male frequency reduced pollen limitation and increased seedset. Coupled with previous studies reporting sex-specific arthropod communities, these results underscore the importance of ecological differences between the sexes in mediating biological responses to climate change.
World Population Day is observed on July 11 every year, when it's time to turn one's attention to the pressing issues of alarming rise in population, awareness about family planning, gender equality and women empowerment. According to United Nations, there are 7.4 billion people occupying the earth at the moment and going by the rapid expansion of population at the moment, it would cross 8 billion by the end of 2023. At least six countries will double their population by 2050, according to USA Today. However, even then, chances are the number of men would be greater than women, a trend that has remained unchanged since ancient times. While the gap between the number of men and women has closed considerably in the last couple of years, it is something that has not occurred uniformly across the world.