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[CT421]: Update Assignment 2 code

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Aindréas Ó hAodha
2025-03-16 14:04:46 +00:00
parent dab6d2f64d
commit f9c7996d7d
1 changed files with 21 additions and 22 deletions
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year4/semester2/CT421/assignments/assignment2/code/ipd.py
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@ -3,6 +3,21 @@
import argparse import argparse
import random import random
# Each strategy is defined as follows:
# [first move, reaction to defection, reaction to co-operation]
# where 0 is defection and 1 is co-operation
# I don't actually know the names for all of these strategies so I'm going to make some up:
strategies = [
[0, 0, 0], # Always defect.
[0, 0, 1], # Grim tit-for-tat.
[0, 1, 0], # Grim opposite day: defect at first, then do opposite of what opponent did last.
[0, 1, 1], # Self-sabotage: defect at first, then always co-operate.
[1, 0, 0], # Feint co-operation, then always defect.
[1, 0, 1], # Tit-for-tat.
[1, 1, 0], # Opposite day: co-operate at first, then do opposite of what opponent did last.
[1, 1, 1] # Always co-operate.
]
def initialise_population(size): def initialise_population(size):
""" """
Initialises a population of strategies for the Iterated Prisoner's Dilemma. Initialises a population of strategies for the Iterated Prisoner's Dilemma.
@ -14,22 +29,6 @@ def initialise_population(size):
population (list): A list of strategies population (list): A list of strategies
""" """
# Each strategy is defined as follows:
# [first move, reaction to defection, reaction to co-operation]
# where 0 is defection and 1 is co-operation
# I don't actually know the names for all of these strategies so I'm going to make some up:
strategies = [
[0, 0, 0], # Always defect.
[0, 0, 1], # Grim tit-for-tat.
[0, 1, 0], # Grim opposite day: defect at first, then do opposite of what opponent did last.
[0, 1, 1], # Self-sabotage: defect at first, then always co-operate.
[1, 0, 0], # Feint co-operation, then always defect.
[1, 0, 1], # Tit-for-tat.
[1, 1, 0], # Opposite day: co-operate at first, then do opposite of what opponent did last.
[1, 1, 1] # Always co-operate.
]
# Since there are only 8 possible strategies, to initialise the population just perform a random over-sampling of the space. # Since there are only 8 possible strategies, to initialise the population just perform a random over-sampling of the space.
return random.choices(strategies, k=size) return random.choices(strategies, k=size)
@ -95,7 +94,7 @@ def fitness(agent, num_iterations):
fixed_strategies = [ fixed_strategies = [
[0, 0, 0], # Always defect. [0, 0, 0], # Always defect.
# [0, 0, 1], # Grim tit-for-tat. # [0, 0, 1], # Grim tit-for-tat.
[0, 1, 0], # Grim opposite day: defect at first, then do opposite of what opponent did last. # [0, 1, 0], # Grim opposite day: defect at first, then do opposite of what opponent did last.
# [0, 1, 1], # Self-sabotage: defect at first, then always co-operate. # [0, 1, 1], # Self-sabotage: defect at first, then always co-operate.
# [1, 0, 0], # Feint co-operation, then always defect. # [1, 0, 0], # Feint co-operation, then always defect.
[1, 0, 1], # Tit-for-tat. [1, 0, 1], # Tit-for-tat.
@ -265,8 +264,8 @@ def evolve(size, num_generations, give_up_after, num_iterations, selection_propo
fitnesses = list_fitnesses(population, num_iterations) fitnesses = list_fitnesses(population, num_iterations)
current_best = get_best(population, fitnesses, 0) current_best = get_best(population, fitnesses, 0)
results = ["Generation\tFitness\tStrategy"] results = ["Generation\tBestFitness\tBestStrategy\tAvgFitness\t000\t001\t010\t011\t100\t101\t110\t111"]
results.append(f"{current_best['generation']}\t{current_best['fitness']}\t{current_best['strategy']}") results.append(f"0\t{current_best['fitness']}\t{"".join(map(str, current_best['strategy']))}\t{sum(fitnesses) / len(fitnesses)}\t{population.count([0,0,0])}\t{population.count([0,0,1])}\t{population.count([0,1,0])}\t{population.count([0,1,1])}\t{population.count([1,0,0])}\t{population.count([1,0,1])}\t{population.count([1,1,0])}\t{population.count([1,1,1])}")
for generation in range(1, num_generations): for generation in range(1, num_generations):
population = tournament_selection(population, fitnesses, int(len(population) *selection_proportion)) population = tournament_selection(population, fitnesses, int(len(population) *selection_proportion))
@ -280,7 +279,7 @@ def evolve(size, num_generations, give_up_after, num_iterations, selection_propo
current_best = generation_best current_best = generation_best
print(f"New best strategy: {current_best['strategy']}, {current_best['fitness']}") print(f"New best strategy: {current_best['strategy']}, {current_best['fitness']}")
results.append(f"{current_best['generation']}\t{current_best['fitness']}\t{current_best['strategy']}") results.append(f"{generation}\t{current_best['fitness']}\t{"".join(map(str, current_best['strategy']))}\t{sum(fitnesses) / len(fitnesses)}\t{population.count([0,0,0])}\t{population.count([0,0,1])}\t{population.count([0,1,0])}\t{population.count([0,1,1])}\t{population.count([1,0,0])}\t{population.count([1,0,1])}\t{population.count([1,1,0])}\t{population.count([1,1,1])}")
if (generation - current_best['generation'] >= give_up_after): if (generation - current_best['generation'] >= give_up_after):
break break
@ -295,11 +294,11 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Program to evolve strategies for the Iterated Prisoner's Dilemma") parser = argparse.ArgumentParser(description="Program to evolve strategies for the Iterated Prisoner's Dilemma")
parser.add_argument("-s", "--size", type=int, help="Initial population size", required=False, default=75) parser.add_argument("-s", "--size", type=int, help="Initial population size", required=False, default=75)
parser.add_argument("-g", "--num-generations", type=int, help="Number of generations", required=False, default=500) parser.add_argument("-g", "--num-generations", type=int, help="Number of generations", required=False, default=500)
parser.add_argument("-a", "--give-up-after", type=int, help="Number of generations to give up after if best solution has remained unchanged", required=False, default=100) parser.add_argument("-a", "--give-up-after", type=int, help="Number of generations to give up after if best solution has remained unchanged", required=False, default=50)
parser.add_argument("-i", "--num-iterations", type=int, help="Number of iterations of the dilemma between two agents", required=False, default=10) parser.add_argument("-i", "--num-iterations", type=int, help="Number of iterations of the dilemma between two agents", required=False, default=10)
parser.add_argument("-p", "--selection-proportion", type=float, help="The proportion of the population to be selected (survive) on each generation", required=False, default=0.2) parser.add_argument("-p", "--selection-proportion", type=float, help="The proportion of the population to be selected (survive) on each generation", required=False, default=0.2)
parser.add_argument("-c", "--crossover-rate", type=float, help="Probability of a selected pair of solutions to sexually reproduce", required=False, default=0.8) parser.add_argument("-c", "--crossover-rate", type=float, help="Probability of a selected pair of solutions to sexually reproduce", required=False, default=0.8)
parser.add_argument("-m", "--mutation-rate", type=float, help="Probability of a selected offspring to undergo mutation", required=False, default=0.2) parser.add_argument("-m", "--mutation-rate", type=float, help="Probability of a selected offspring to undergo mutation", required=False, default=0.1)
parser.add_argument("-o", "--output-file", type=str, help="File to write TSV results to", required=False, default="output.tsv") parser.add_argument("-o", "--output-file", type=str, help="File to write TSV results to", required=False, default="output.tsv")
args=parser.parse_args() args=parser.parse_args()