I would suggest before continuing to read the excellent blog post by Martin Jones.
The LookerUp strategy uses a 64-key lookup table (keys are 3-tuples consisting of the opponent's starting actions, the opponent's recent actions, and our recent action) to decide whether to cooperate (C) or defect (D). The actions for each key were generated using an evolutionary algorithm.
Instead of having a binary action for each key, we could have a number between 0 and 1 that gives the probability for the decision. We will use the function random_choice from Axelrod Library with random_choice(0)=D and random_choice(1)=C.
To accomodate the change, some code needed to change in the LookerUp strategy created by Martin Jones. The pattern in EvolvedLookerUp class needed to change into a list of numbers.
# Original pattern
pattern_orginal = 'CDCCDCCCDCDDDDDCCDCCDDDDDCDCDDDCDDDDCCCDDCCDDDDDCDCDDDCDCDDDDDDD'
# Changed into numbers
pattern_original_number = '1011011101000001101100000101000100001110011000001010001010000000'
# Changed into a list
pattern_original_list = [1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,
1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,
0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,
1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
Now instead of returning a action, we add the random_choice function.
# Original
action = self.lookup_table[key]
return action
# Changed
action = float(self.lookup_table[key])
return random_choice(action)
Both the original (Evolved) and the changed (EvolvedG) were run against the other strategies to test if anything changed. Based on a few random elements in the opponents strategies, the output should look similar but not the same.
We change the score_for function from Martin's blog to request a pattern that is then passed when creating our strategy.
def score_for_pattern(my_strategy_factory,pattern, iterations=200):
"""
Given a function that will return a strategy,
calculate the average score per turn
against all ordinary strategies. If the
opponent is classified as stochastic, then
run 100 repetitions and take the average to get
a good estimate.
"""
scores_for_all_opponents = []
for opponent in axelrod.ordinary_strategies:
# decide whether we need to sample or not
if opponent.classifier['stochastic']:
repetitions = 100
else:
repetitions = 1
scores_for_this_opponent = []
# calculate an average for this opponent
for _ in range(repetitions):
me = my_strategy_factory(pattern)
other = opponent()
# make sure that both players know what length the match will be
me.set_tournament_attributes(length=iterations)
other.set_tournament_attributes(length=iterations)
scores_for_this_opponent.append(score_single(me, other, iterations))
mean_vs_opponent = sum(scores_for_this_opponent) / len(scores_for_this_opponent)
scores_for_all_opponents.append(mean_vs_opponent)
# calculate the average for all opponents
overall_average_score = sum(scores_for_all_opponents) / len(scores_for_all_opponents)
return(overall_average_score)
And create our strategy called TestGambler that requires a pattern.
class Gambler(Player):
name = 'Gambler'
classifier = {
'memory_depth': float('inf'),
'stochastic': True,
'makes_use_of': set(),
'inspects_source': False,
'manipulates_source': False,
'manipulates_state': False
}
@init_args
def __init__(self, lookup_table=None):
"""
If no lookup table is provided to the constructor, then use the TFT one.
"""
Player.__init__(self)
if not lookup_table:
lookup_table = {
('', 'C', 'D') : 0,
('', 'D', 'D') : 0,
('', 'C', 'C') : 1,
('', 'D', 'C') : 1,
}
self.lookup_table = lookup_table
# Rather than pass the number of previous turns (m) to consider in as a
# separate variable, figure it out. The number of turns is the length
# of the second element of any given key in the dict.
self.plays = len(list(self.lookup_table.keys())[0][1])
# The number of opponent starting actions is the length of the first
# element of any given key in the dict.
self.opponent_start_plays = len(list(self.lookup_table.keys())[0][0])
# If the table dictates to ignore the opening actions of the opponent
# then the memory classification is adjusted
if self.opponent_start_plays == 0:
self.classifier['memory_depth'] = self.plays
# Ensure that table is well-formed
for k, v in lookup_table.items():
if (len(k[1]) != self.plays) or (len(k[0]) != self.opponent_start_plays):
raise ValueError("All table elements must have the same size")
def strategy(self, opponent):
# If there isn't enough history to lookup an action, cooperate.
if len(self.history) < max(self.plays, self.opponent_start_plays):
return C
# Count backward m turns to get my own recent history.
history_start = -1 * self.plays
my_history = ''.join(self.history[history_start:])
# Do the same for the opponent.
opponent_history = ''.join(opponent.history[history_start:])
# Get the opponents first n actions.
opponent_start = ''.join(opponent.history[:self.opponent_start_plays])
# Put these three strings together in a tuple.
key = (opponent_start, my_history, opponent_history)
# Look up the action associated with that tuple in the lookup table.
action = float(self.lookup_table[key])
return random_choice(action)
class TestGambler(Gambler):
"""
A LookerUp strategy that uses pattern supplied when initialised.
"""
name = "TestGambler"
def __init__(self,pattern):
plays = 2
opponent_start_plays = 2
# Generate the list of possible tuples, i.e. all possible combinations
# of m actions for me, m actions for opponent, and n starting actions
# for opponent.
self_histories = [''.join(x) for x in product('CD', repeat=plays)]
other_histories = [''.join(x) for x in product('CD', repeat=plays)]
opponent_starts = [''.join(x) for x in
product('CD', repeat=opponent_start_plays)]
lookup_table_keys = list(product(opponent_starts, self_histories,
other_histories))
# Zip together the keys and the action pattern to get the lookup table.
lookup_table = dict(zip(lookup_table_keys, pattern))
Gambler.__init__(self, lookup_table=lookup_table)
We use a python library called pyswarm to perform the PSO. We set the constrain to be between (0,1) for our parameters. with lb and ub. We then try to minimise the function optimise_pso, running the pso function which outputs the numbers for the parameters (xopt) and the score of the function (fopt).
from pyswarm import pso
lb = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
ub = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
def optimizepso(x):
return -score_for_pattern(TestGambler,x)
# The parameters of phip, phig and omega will lead to slower conversion
xopt, fopt = pso(optimizepso, lb, ub, swarmsize=100, maxiter=20, processes=60, debug=True,
phip=0.8, phig=0.8, omega=0.8)
Running the optimisation led to the following numbers
pattern_pso = [1.0 ,0.0,1.0,1.0 ,0.0 ,1.0,1.0,1.0,0.0 ,1.0 ,0.0,0.0,0.0,0.0,0.0,1.0 ,
0.93,0.0,1.0,0.67,0.42,0.0,0.0,0.0,0.0 ,1.0 ,0.0,1.0,0.0,0.0,0.0,0.48,
0.0 ,0.0,0.0,0.0 ,1.0 ,1.0,1.0,0.0,0.19,1.0 ,1.0,0.0,0.0,0.0,0.0,0.0 ,
1.0 ,0.0,1.0,0.0 ,0.0 ,0.0,1.0,0.0,1.0 ,0.36,0.0,0.0,0.0,0.0,0.0,0.0 ]
# The parameters are the same as the EvolvedLookerUp except for
# OpStart, SelfLast2, OpLast2
#('CD', 'DD', 'DD'): 0.48 # Occurs 0.9%
#('CD', 'CC', 'DD'): 0.67 # Occurs 0.3%
#('DC', 'DC', 'CC'): 0.19 # Occurs 0.4%
#('CD', 'CC', 'CC'): 0.93 # Occurs 1.0%
#('CD', 'CD', 'CC'): 0.42 # Occurs 0.1%
#('DD', 'DC', 'CD'): 0.36 # Occurs 0.0%
We now run our new strategy (Gambler) with the pattern_pso list against the other strategies
If we specify always to defect the last two turns, we get better results.
MIT License
Copyright (c) 2016 Georgios Koutsovoulos
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Could someone put the entire improved code for GamblerBS in a reply? Thanks!