def __init__(self, four_vector=None, initial=C):
"""
Parameters
fourvector, list or tuple of floats of length 4
The response probabilities to the preceeding round of play
( P(C|CC), P(C|CD), P(C|DC), P(C|DD) )
initial, C or D
The initial move
Special Cases
Alternator is equivalent to MemoryOnePlayer((0, 0, 1, 1), C)
Cooperator is equivalent to MemoryOnePlayer((1, 1, 1, 1), C)
Defector is equivalent to MemoryOnePlayer((0, 0, 0, 0), C)
Random is equivalent to MemoryOnePlayer((0.5, 0.5, 0.5, 0.5))
(with a random choice for the initial state)
TitForTat is equivalent to MemoryOnePlayer((1, 0, 1, 0), C)
WinStayLoseShift is equivalent to MemoryOnePlayer((1, 0, 0, 1), C)
See also: The remaining strategies in this file
Multiple strategies in titfortat.py
Grofman, Joss in axelrod_tournaments.py
"""
Player.__init__(self)
self._initial = initial
if four_vector:
self.set_four_vector(four_vector)
def __init__(self):
Player.__init__(self)
self.calming = False
self.punishing = False
self.punishment_count = 0
self.punishment_limit = 0
def __init__(self, memory_depth=float('inf'), soft=True):
"""
Parameters
----------
memory_depth, int >= 0
The number of rounds to use for the calculation of the cooperation
and defection probabilities of the opponent.
soft, bool
Indicates whether to cooperate or not in the case that the
cooperation and defection probabilities are equal.
"""
Player.__init__(self)
self.soft = soft
self.classifier['memory_depth'] = memory_depth
if self.classifier['memory_depth'] < float('inf'):
self.memory = self.classifier['memory_depth']
else:
self.memory = 0
self.name = (
'Go By Majority' + (self.memory > 0) * (": %i" % self.memory))
if self.soft:
self.name = "Soft " + self.name
else:
self.name = "Hard " + self.name
def __init__(self):
"""
Uses the basic init from the Player class, but also set the name to
include the retaliation setting.
"""
Player.__init__(self)
self.name = ('Retaliate (' + str(self.retaliation_threshold) + ')')
def __init__(self, lookup_table=None, value_length=1):
"""
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'): D,
('', 'D', 'D'): D,
('', 'C', 'C'): C,
('', 'D', 'C'): C,
}
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")
if value_length is not None:
if len(v) > value_length:
raise ValueError("Table values should be of length one, C or D")
def __init__(self, four_vector=None, initial=C):
"""
Parameters
----------
fourvector, list or tuple of floats of length 4
The response probabilities to the preceeding round of play
( P(C|CC), P(C|CD), P(C|DC), P(C|DD) )
initial, C or D
The initial move
Special Cases
-------------
Alternator is equivalent to MemoryOnePlayer((0, 0, 1, 1), C)
Cooperator is equivalent to MemoryOnePlayer((1, 1, 1, 1), C)
Defector is equivalent to MemoryOnePlayer((0, 0, 0, 0), C)
Random is equivalent to MemoryOnePlayer((0.5, 0.5, 0.5, 0.5))
(with a random choice for the initial state)
TitForTat is equivalent to MemoryOnePlayer((1, 0, 1, 0), C)
WinStayLoseShift is equivalent to MemoryOnePlayer((1, 0, 0, 1), C)
See also: The remaining strategies in this file
Multiple strategies in titfortat.py
Grofman, Joss in axelrod_tournaments.py
"""
Player.__init__(self)
self._initial = initial
if four_vector:
self.set_four_vector(four_vector)
def reset(self):
"""
Resets scores and history
"""
Player.reset(self)
self.grudged = False
self.grudge_memory = 0
def __init__(self):
Player.__init__(self)
self.calming = False
self.punishing = False
self.punishment_count = 0
self.punishment_limit = 0
def __init__(self, memory_depth = 3, exploreProb = 0.2, learnerType = 1):
"""
Parameters
----------
memory_depth, int >= 0
The number of rounds to use for the calculation of the cooperation
and defection probabilities of the opponent.
exploreProb, float >= 0
The probability of exploration while ignoring the best option
"""
Player.__init__(self)
self.type = learnerType
self.qTabSize = (4**memory_depth)
# qTabSize : It refers to the size of the qTable. It consists of all possible states in the game
self.qTab = [dict({True: 2, False: 2}) for i in range(0, self.qTabSize)]
# qTab : The QTable which stores the Q values. It's updated during gameplay
self.turns = [dict({True: 1, False: 1}) for i in range(0, self.qTabSize)]
self.totTurns = 0
self.classifier['memory_depth'] = memory_depth
# The memory depth to consider while playing the game
self.memory = self.classifier['memory_depth']
self.explore = exploreProb
# Initialize the payoff matrix for the game
(R, P, S, T) = self.tournament_attributes["game"].RPST()
self.payoff = {C: {C: R, D: S}, D: {C: T, D: P}}
self.prevState = 0
self.prevAction = False
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') : D,
('', 'D', 'D') : D,
('', 'C', 'C') : C,
('', 'D', 'C') : C,
}
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")
if len(v) > 1:
raise ValueError("Table values should be of length one, C or D")
def reset(self):
"""
Resets scores and history
"""
Player.reset(self)
self.grudged = False
self.grudge_memory = 0
def __init__(self, memory_depth=float('inf'), soft=True):
"""
Parameters
----------
memory_depth, int >= 0
The number of rounds to use for the calculation of the cooperation
and defection probabilities of the opponent.
soft, bool
Indicates whether to cooperate or not in the case that the
cooperation and defection probabilities are equal.
"""
Player.__init__(self)
self.soft = soft
self.classifier['memory_depth'] = memory_depth
if self.classifier['memory_depth'] < float('inf'):
self.memory = self.classifier['memory_depth']
else:
self.memory = 0
self.name = 'Go By Majority' + (self.memory > 0) * (": %i" % self.memory)
if self.soft:
self.name = "Soft " + self.name
else:
self.name = "Hard " + self.name
def __init__(self,
start_coop_prob=1.0,
end_coop_prob=0.5,
rounds_of_decay=200):
Player.__init__(self)
self._start_coop_prob = start_coop_prob
self._end_coop_prob = end_coop_prob
self._rounds_of_decay = rounds_of_decay
def __init__(self, cycle="CCD"):
"""This strategy will repeat the parameter `cycle` endlessly,
e.g. C C D C C D C C D ..."""
Player.__init__(self)
self.cycle = cycle
self.name += " " + cycle
self.classifier['memory_depth'] = len(cycle) - 1
self.init_args = (cycle,)
def __init__(self):
Player.__init__(self)
self.init_sequence = [
C, C, D, C, D, D, D, C, C, D, C, D, C, C, D, C, D, D, C, D
]
self.just_Ds = 0
self.unjust_Ds = 0
self.turned_defector = False
def reset(self):
Player.reset(self)
self.good = 1.0
self.bad = 0.0
self.nice1 = 0
self.nice2 = 0
self.total_C = 0 # not the same as self.cooperations
self.total_D = 0 # not the same as self.defections
def __init__(self, player, move):
# Need to retain history for opponents that examine opponents history
# Do a deep copy just to be safe
Player.__init__(self)
self.history = copy.deepcopy(player.history)
self.cooperations = player.cooperations
self.defections = player.defections
self.move = move
def reset(self):
Player.reset(self)
self.good = 1.0
self.bad = 0.0
self.nice1 = 0
self.nice2 = 0
self.total_C = 0 # not the same as self.cooperations
self.total_D = 0 # not the same as self.defections
def __init__(self, four_vector, initial='C'):
Player.__init__(self)
self._four_vector = dict(zip([('C', 'C'), ('C', 'D'), ('D', 'C'), ('D', 'D')], map(float, four_vector)))
self._initial = initial
self.stochastic = False
for x in set(four_vector):
if x != 0 and x != 1:
self.stochastic = True
def __init__(self, player, move):
# Need to retain history for opponents that examine opponents history
# Do a deep copy just to be safe
Player.__init__(self)
self.history = copy.deepcopy(player.history)
self.cooperations = player.cooperations
self.defections = player.defections
self.move = move
def __init__(self, retaliation_threshold=0.1):
"""
Uses the basic init from the Player class, but also set the name to
include the retaliation setting.
"""
Player.__init__(self)
self.retaliation_threshold = retaliation_threshold
self.name = ('Retaliate (' + str(self.retaliation_threshold) + ')')
self.play_counts = defaultdict(int)
def __init__(self):
"""
Uses the basic init from the Player class, but also set the name to
include the retaliation setting.
"""
Player.__init__(self)
self.name = (
'Retaliate (' +
str(self.retaliation_threshold) + ')')
def __init__(self, revised=True):
Player.__init__(self)
self.revised = revised
self.good = 1.0
self.bad = 0.0
self.nice1 = 0
self.nice2 = 0
self.total_C = 0 # note the same as self.cooperations
self.total_D = 0 # note the same as self.defections
def __init__(self, rounds_to_cooperate=10):
"""
Parameters
----------
rounds_to_cooperate: int, 10
The number of rounds to cooperate initially
"""
Player.__init__(self)
self._rounds_to_cooperate = rounds_to_cooperate
def __init__(self, start_coop_prob=1.0, end_coop_prob=0.5,
rounds_of_decay=200):
Player.__init__(self)
self._start_coop_prob = start_coop_prob
self._end_coop_prob = end_coop_prob
self._rounds_of_decay = rounds_of_decay
self.init_args = (start_coop_prob,
end_coop_prob,
rounds_of_decay)
def __init__(self, revised=True):
Player.__init__(self)
self.revised = revised
self.good = 1.0
self.bad = 0.0
self.nice1 = 0
self.nice2 = 0
self.total_C = 0 # note the same as self.cooperations
self.total_D = 0 # note the same as self.defections
def __init__(self, transitions=None, initial_state=None, initial_action=None):
if not transitions:
# Tit For Tat
transitions = [(1, C, 1, C), (1, D, 1, D)]
initial_state = 1
initial_action = C
Player.__init__(self)
self.initial_action = initial_action
self.fsm = SimpleFSM(transitions, initial_state)
def __init__(self, rounds_to_cooperate=10):
"""
Parameters
----------
rounds_to_cooperate: int, 10
The number of rounds to cooperate initially
"""
Player.__init__(self)
self._rounds_to_cooperate = rounds_to_cooperate
def __init__(self, transitions=None, initial_state=None, initial_action=None):
if not transitions:
# Tit For Tat
transitions = [(1, C, 1, C), (1, D, 1, D)]
initial_state = 1
initial_action = C
Player.__init__(self)
self.initial_state = initial_state
self.initial_action = initial_action
self.fsm = SimpleFSM(transitions, initial_state)
def __init__(self, rounds_to_cooperate=11):
"""
Parameters
----------
rounds_to_cooperate: int, 10
The number of rounds to cooperate initially
"""
Player.__init__(self)
self._rounds_to_cooperate = rounds_to_cooperate
self.__class__.memory_depth = rounds_to_cooperate
def __init__(self, initial='C'):
Player.__init__(self)
self.response_dict = {
('C', 'C'): 'C',
('C', 'D'): 'D',
('D', 'C'): 'D',
('D', 'D'): 'C',
}
self._initial = initial
self.stochastic = False
def reset(self):
"""
Resets scores and history
"""
Player.reset(self)
self.Qs = {'': {C: 0, D: 0}}
self.Vs = {'': 0}
self.prev_state = ''
self.prev_action = random_choice()
def __init__(self, rounds_to_cooperate=11):
"""
Parameters
----------
rounds_to_cooperate: int, 10
The number of rounds to cooperate initially
"""
Player.__init__(self)
self._rounds_to_cooperate = rounds_to_cooperate
self.__class__.memory_depth = rounds_to_cooperate
def __init__(self, initial='C'):
Player.__init__(self)
self.response_dict = {
('C', 'C'): 'C',
('C', 'D'): 'D',
('D', 'C'): 'D',
('D', 'D'): 'C',
}
self._initial = initial
self.stochastic = False
def __init__(self, p=0.5):
"""
Parameters
----------
p, float
The probability to cooperate
"""
Player.__init__(self)
self.p = p
self.init_args = (p,)
def reset(self):
"""
Resets scores and history
"""
Player.reset(self)
self.Qs = {'': {C: 0, D: 0}}
self.Vs = {'': 0}
self.prev_state = ''
self.prev_action = random_choice()
def __init__(self, four_vector=[1, 0, 0, 1], initial='C'):
Player.__init__(self)
self._four_vector = dict(
zip([('C', 'C'), ('C', 'D'), ('D', 'C'), ('D', 'D')],
map(float, four_vector)))
self._initial = initial
self.stochastic = False
for x in set(four_vector):
if x != 0 and x != 1:
self.stochastic = True
def __init__(self, retaliation_threshold=0.1):
"""
Uses the basic init from the Player class, but also set the name to
include the retaliation setting.
"""
Player.__init__(self)
self.retaliation_threshold = retaliation_threshold
self.name = (
'Retaliate (' +
str(self.retaliation_threshold) + ')')
self.play_counts = defaultdict(int)
def __init__(self, forget_probability=0.05):
"""
Parameters
----------
forget_probability, float
The probability of forgetting the count of opponent defections.
"""
Player.__init__(self)
self.D_count = 0
self._initial = C
self.forget_probability = forget_probability
def __init__(self, p=0.1):
"""
Parameters
----------
p, float
The probability to defect randomly
"""
Player.__init__(self)
self.p = p
if (self.p == 0) or (self.p == 1):
self.classifier['stochastic'] = False
def test_strategy(self):
for action in [C, D]:
m = MockPlayer(actions=[action])
p2 = Player()
self.assertEqual(action, m.strategy(p2))
actions = [C, C, D, D, C, C]
m = MockPlayer(actions=actions)
p2 = Player()
for action in actions:
self.assertEqual(action, m.strategy(p2))
def reset(self):
"""
Resets scores and history
"""
Player.reset(self)
self.Q = OrderedDict()
self.Nsa = OrderedDict()
self.prev_state = None
self.prev_action = None
self.n = 1
self.prob = []
def reset(self):
"""
Resets scores, QTable and history
"""
Player.reset(self)
self.qTab = [dict({True: 0, False: 0}) for i in range(0, self.qTabSize)]
self.turns = [dict({True: 0, False: 0}) for i in range(0, self.qTabSize)]
self.totTurns = 0
self.prevState = 0
self.prevAction = False
def __init__(self, initial='C'):
Player.__init__(self)
self.response_dict = {
('C', 'C'): 'C',
('C', 'D'): 'D',
('D', 'C'): 'D',
('D', 'D'): 'C',
}
self.classifier['stochastic'] = False
self._initial = initial
self.init_args = (initial,)
def __init__(self, forget_probability = 0.05):
"""
Parameters
----------
forget_probability, float
The probability of forgetting the count of opponent defections.
"""
Player.__init__(self)
self.D_count = 0
self._initial = C
self.forget_probability = forget_probability
def responses_test(self, history_1, history_2, responses, random_seed=None, tournament_length=200):
"""Test responses to arbitrary histories. Input response_list is a
list of lists, each of which consists of a list for the history of
player 1, a list for the history of player 2, and a list for the
subsequent moves by player one to test.
"""
P1 = self.player()
P1.tournament_attributes["length"] = tournament_length
P2 = Player()
P2.tournament_attributes["length"] = tournament_length
test_responses(self, P1, P2, history_1, history_2, responses, random_seed=random_seed)
def __init__(self):
Player.__init__(self)
# Make sure we don't use any meta players to avoid infinite recursion.
self.team = [t for t in self.team if not issubclass(t, MetaPlayer)]
self.nteam = len(self.team)
# Initiate all the player in out team.
self.team = [t() for t in self.team]
# If the team will have stochastic players, this meta is also stochastic.
self.stochastic = any([t.stochastic for t in self.team])
def __init__(self):
Player.__init__(self)
# Make sure we don't use any meta players to avoid infinite recursion.
self.team = [t for t in self.team if not issubclass(t, MetaPlayer)]
self.nteam = len(self.team)
# Initiate all the player in out team.
self.team = [t() for t in self.team]
# If the team will have stochastic players, this meta is also stochastic.
self.stochastic = any([t.stochastic for t in self.team])
def __init__(self, p=0.5):
"""
Parameters
----------
p, float
The probability to cooperate
Special Cases
-------------
Random(0) is equivalent to Defector
Random(1) is equivalent to Cooperator
"""
Player.__init__(self)
self.p = p
def __init__(self, memory_depth = 3, action_codes = ([False]*70)):
"""
Parameters
----------
memory_depth, int >= 0
The number of rounds to use for the calculation of the cooperation
and defection probabilities of the opponent.
action_codes, bool list
Indicates the exact action to perform based on the other players' history.
"""
Player.__init__(self)
self.actions = action_codes
self.classifier['memory_depth'] = memory_depth
self.memory = self.classifier['memory_depth']
def __init__(self, cycle="CCD"):
"""This strategy will repeat the parameter `cycle` endlessly,
e.g. C C D C C D C C D ...
Special Cases
-------------
Cooperator is equivalent to Cycler("C")
Defector is equivalent to Cycler("D")
Alternator is equivalent to Cycler("CD")
"""
Player.__init__(self)
self.cycle = cycle
self.name += " " + cycle
self.classifier['memory_depth'] = len(cycle) - 1
def __init__(self, cycle="CCD"):
"""This strategy will repeat the parameter `cycle` endlessly,
e.g. C C D C C D C C D ...
Special Cases
-------------
Cooperator is equivalent to Cycler("C")
Defector is equivalent to Cycler("D")
Alternator is equivalent to Cycler("CD")
"""
Player.__init__(self)
self.cycle = cycle
self.name = "Cycler {}".format(cycle)
self.classifier['memory_depth'] = len(cycle) - 1
def __init__(self, memory_depth=3, action_codes=([False] * 70)):
"""
Parameters
----------
memory_depth, int >= 0
The number of rounds to use for the calculation of the cooperation
and defection probabilities of the opponent.
action_codes, bool list
Indicates the exact action to perform based on the other players' history.
"""
Player.__init__(self)
self.actions = action_codes
self.classifier['memory_depth'] = memory_depth
self.memory = self.classifier['memory_depth']
def __init__(self, four_vector=None, initial='C'):
"""
Parameters
----------
fourvector, list or tuple of floats of length 4
The response probabilities to the preceeding round of play
( P(C|CC), P(C|CD), P(C|DC), P(C|DD) )
initial, 'C' or 'D'
The initial move
"""
Player.__init__(self)
self._initial = initial
if four_vector:
self.set_four_vector(four_vector)
self.init_args = (four_vector, initial)
def __init__(self, p=0.5):
"""
Parameters
----------
p, float
The probability to cooperate
Special Cases
-------------
Random(0) is equivalent to Defector
Random(1) is equivalent to Cooperator
"""
Player.__init__(self)
self.p = p
if p in [0, 1]:
self.classifier['stochastic'] = False
def __init__(self, name='Human', c_symbol='C', d_symbol='D'):
"""
Parameters
----------
name: string
The name of the human player
c_symbol: string
A symbol to denote cooperation within the history toolbar
and prompt
d_symbol: string
A symbol to denote defection within the history toolbar
and prompt
"""
Player.__init__(self)
self.name = name
self.symbols = {C: c_symbol, D: d_symbol}
self.opponent_history = []
