In addition to the other explanation, check out today's NYT article on how one guy cracked the lottery because of pseudo-random behaviour in the lottery code.
Most random sources are PRNG rather than 'true' random sources, and sometimes it's useful (for debugging, for analysis or just for interest) to be able to use a predictable pattern of otherwise random numbers.
One way is to allow some way of 'seeding' the PRNG such that the order of the numbers it produces is the same each time, as we return the random function back to a known state.
Or, by example, if I make 5 calls to the PRNG with seed value '0' and see the following: [5, 2, 9, 18, 4, ...] and that causes the agent I'm testing to do something utterly weird, so I want to re-run my agent to observe the effect in detail to debug it, and for that to happen, I need the same [5, 2, 9, 18, 4, ...] sequence, otherwise I'll be forced to run repeatedly until I observe the same glitch, so by re-seeding the PRNG to '0', it will then predictably return that sequence, rather than a new, random sequence.
It's because most of the randomness used by software is actually pseudorandom. What that means is that you actually use a defined sequence. The sequence has behaviour that's close enough to what you'd get if you were picking random samples from a distribution for the desired application.
The key difference is that it's reproducible and that if you have insight into the parameters of the sequence (e.g. the seed and the current position in the sequence), you can predict the results. That's why people often get upset when people use these pseudorandom number generators for security purposes.
The seed is a value that is used to generate the sequence. If you use the same seed, you get the same sequence.
Typically when you init a random generator, it'll let you pass a number in if you want to. That will set the sequence of "random" output from the generator; different seeds will be random with respect to each other. If you re-use the same seed you'll get the same sequence of "random" numbers as before. This is useful to test or re-try sequences involving "random" in a reproducible way.
https://www.nytimes.com/interactive/2018/05/03/magazine/mone...