atari_genetic_evolution.py

def genetic_evolution(self, env):
        print "population_size: " + str(self.population_size) +\
              ", mutation_rate: " + str(self.mutation_rate) +\
              ", selection_rate: " + str(self.selection_rate) +\
              ", random_weight_range: " + str(self.random_weight_range)
        population = None

        while True:
            print('{{"metric": "generation", "value": {}}}'.format(self.generation))

            # 1. Selection
            parents = self._strongest_parents(population, env)

            self._save_model(parents)  # Saving main model based on the current best two chromosomes

            # 2. Crossover (Roulette selection)
            pairs = []
            while len(pairs) != self.population_size:
                pairs.append(self._pair(parents))

            base_offsprings = []
            for pair in pairs:
                offsprings = self._crossover(pair[0][0], pair[1][0])
                base_offsprings.append(offsprings[-1])

            # 3. Mutation
            new_population = self._mutation(base_offsprings)
            population = new_population
            self.generation += 1