There are many ways to implement evolution but here is a simple one :
For every kind of agent (herbivorous, carnivorous) :
Each worker is an individual with carecteristics (speed, size,... )
Every N (= 10000 for example or 1000) steps a selection+reproduction is done : it select the 5 individual with the best reward, do the crossover and produce 50 new individual, one by worker.
Reinforcement learning procedure stay the same.
On death and reset of an individual during an evolution step, take the same carecteristics (the mean reward is considered at the end of the evolution step)
There are many ways to implement evolution but here is a simple one : For every kind of agent (herbivorous, carnivorous) : Each worker is an individual with carecteristics (speed, size,... ) Every N (= 10000 for example or 1000) steps a selection+reproduction is done : it select the 5 individual with the best reward, do the crossover and produce 50 new individual, one by worker. Reinforcement learning procedure stay the same.
On death and reset of an individual during an evolution step, take the same carecteristics (the mean reward is considered at the end of the evolution step)