Tournament Selection

Tournament selection is used to select the agents from a population which will make up the next generation of agents. If elitism is used, the best agent from a population is automatically preserved and becomes a member of the next generation. Then, for each tournament, k individuals are randomly chosen, and the agent with the best evaluation fitness is preserved. This is repeated until the population for the next generation is full.

The class TournamentSelection defines the functions required for tournament selection. TournamentSelection.select() returns the best agent, the new generation of agents, and the indices of the agents to mutate.

The new generation is then perturbed by the shared mutation step to explore the hyperparameter space; under tournament selection every agent in the new generation is eligible for mutation.

from agilerl.hpo.tournament import TournamentSelection

tournament = TournamentSelection(
    tournament_size=2,  # Tournament selection size
    elitism=True,  # Elitism in tournament selection
    population_size=6,  # Population size
)