def test_return_values(self):
self.assertEqual(random_choice(1), C)
self.assertEqual(random_choice(0), D)
random.seed(1)
self.assertEqual(random_choice(), C)
random.seed(2)
self.assertEqual(random_choice(), D)
def test_seed_not_offset_by_deterministic_call(self):
"""Test that when called with p = 0 or 1, the random seed is not
affected."""
for p in [0, 1]:
seed(0)
r = random.random()
seed(0)
random_choice(p)
self.assertEqual(r, random.random())
def strategy(self, opponent: Player) -> Action:
# First turn
if len(self.history) == 0:
return random_choice(self._c_prob / (self._c_prob + self._d_prob))
# Updating stimulus depending on his own latest choice
self.stimulus_update(opponent)
return random_choice(self._c_prob / (self._c_prob + self._d_prob))
def strategy(self, opponent):
if not opponent.history:
return C
if opponent.history[-1] == D:
return D
p = self._cooperation_probability()
return random_choice(p)
def strategy(self, opponent):
# Random first move
if not self.history:
return random_choice();
# Act opposite of opponent otherwise
return flip_action(opponent.history[-1])
def strategy(self, opponent):
if not opponent.history:
return C
if opponent.history[-1] == D:
return D
p = self._cooperation_probability()
return random_choice(p)
class Geller(Player):
"""Observes what the player will do in the next round and adjust.
If unable to do this: will play randomly.
"""
name = 'Geller'
default = lambda self: random_choice(0.5)
classifier = {
'memory_depth': -1,
'stochastic': True,
'makes_use_of': set(),
'inspects_source': True, # Finds out what opponent will do
'manipulates_source': False,
'manipulates_state': False
}
def strategy(self, opponent):
"""
Look at what the opponent will play in the next round and choose a strategy
that gives the least jail time, which is is equivalent to playing the same
strategy as that which the opponent will play.
"""
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
calname = calframe[1][3]
if calname == 'strategy':
return self.default()
else:
return opponent.strategy(self)
def strategy(self, opponent):
# Random first move
if not self.history:
return random_choice()
# Act opposite of opponent otherwise
return flip_action(opponent.history[-1])
def strategy(self, opponent):
"""
A player chooses the best action based on qTab a predefined array
of choices. It considers the k previous moves and decides which move
to make based on the computed Q Table.
The length of the state list is 4^mem
"""
mem = self.memory
randomPlay = False
opHistory = opponent.history[-mem:]
myHistory = self.history[-mem:]
if ((len(opHistory) < mem) or (len(myHistory) < mem)):
randomPlay = True
# In case the memory isn't enough, play a random move
if (randomPlay):
return random_choice()
# print (self.prevState, self.prevAction, opponent.history[-1], self.payoff[decodeMove(self.prevAction)][opponent.history[-1]])
# Update the q table when results of the previous turn are available
self.qTabUpdate(self.prevState, self.prevAction, self.payoff[decodeMove(self.prevAction)][opponent.history[-1]])
choice = []
for i in range(0,mem):
choice.append(encode(myHistory[i], opHistory[i]))
# Get the encoding for the state
ids = decode(choice, mem)
# print ids
if (self.type > 0):
self.totTurns += 1
# if (self.totTurns%1000 == 0):
# print self.totTurns, self.explore*correction(self.totTurns)
self.prevState = ids
if (random.random() < self.explore*correction(self.totTurns)):
self.prevAction = encodeMove(random_choice())
# print self.prevAction
elif (self.type == 1):
self.prevAction = max(self.qTab[ids].iteritems(), key=operator.itemgetter(1))[0]
else:
self.prevAction = numpy.random.choice(self.qTab[ids].keys(), p = sigmoid(self.qTab[ids].values()))
return decodeMove(self.prevAction)
def strategy(self, opponent):
rounds = self._rounds_to_cooperate
if len(self.history) < rounds:
return C
cooperate_count = opponent.history[-rounds:].count(C)
prop_cooperate = cooperate_count / float(rounds)
prob_cooperate = max(0, prop_cooperate - 0.10)
return random_choice(prob_cooperate)
def strategy(self, opponent):
rounds = self._rounds_to_cooperate
if len(self.history) < rounds:
return C
cooperate_count = opponent.history[-rounds:].count(C)
prop_cooperate = cooperate_count / float(rounds)
prob_cooperate = max(0, prop_cooperate - 0.10)
return random_choice(prob_cooperate)
def strategy(self, opponent):
current_round = len(self.history) + 1
if current_round == 1:
return C
else:
probability = 1 - opponent.defections / (current_round - 1)
return random_choice(probability)
def strategy(self, opponent):
round_number = len(self.history) + 1
if round_number < 3:
return C
if round_number < 8:
return opponent.history[-1]
if self.history[-1] == opponent.history[-1]:
return C
return random_choice(2./ 7)
def strategy(self, opponent):
if not hasattr(self, "_four_vector"):
raise ValueError("_four_vector not yet set")
if len(opponent.history) == 0:
return self._initial
# Determine which probability to use
p = self._four_vector[(self.history[-1], opponent.history[-1])]
# Draw a random number in [0,1] to decide
return random_choice(p)
def select_action(self, state):
"""
Selects the action based on the epsilon-soft policy
"""
rnd_num = random.random()
p = 1. - self.action_selection_parameter
if rnd_num < p:
return max(self.Qs[state], key=lambda x: self.Qs[state][x])
return random_choice()
def strategy(self, opponent):
if not hasattr(self, "_four_vector"):
raise ValueError("_four_vector not yet set")
if len(opponent.history) == 0:
return self._initial
# Determine which probability to use
p = self._four_vector[(self.history[-1], opponent.history[-1])]
# Draw a random number in [0,1] to decide
return random_choice(p)
def strategy(self, opponent):
round_number = len(self.history) + 1
if round_number < 3:
return C
if round_number < 8:
return opponent.history[-1]
if self.history[-1] == opponent.history[-1]:
return C
return random_choice(2. / 7)
def select_action(self, state):
"""
Selects the action based on the epsilon-soft policy
"""
rnd_num = random.random()
p = 1. - self.action_selection_parameter
if rnd_num < p:
return max(self.Qs[state], key=lambda x: self.Qs[state][x])
return random_choice()
def strategy(opponent):
"""Randomly picks a strategy (not affected by history)."""
if len(opponent.history) == 0:
return random_choice(0.5)
p = opponent.cooperations // len(opponent.history)
rnd_num = random.random()
if rnd_num < p:
return 'C'
return 'D'
def select_action(self, state):
if state == '':
return random_choice()
if self.Q[state][D] > self.Q[state][C]:
action = D
else:
action = C
rnd_num = random.random()
p = self.boltzman(state, action)
self.prob.append(p)
#print ("p=",p)
if rnd_num < p:
return action
else:
#print "random choice"
return random_choice()
def strategy(self, opponent):
# First move
if len(self.history) == 0:
return C
# React to the opponent's last move
if opponent.history[-1] == D:
return D
# Otherwise cooperate, defect with a small probability
choice = random_choice(1 - self.p)
return choice
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 find_reward(self, opponent):
"""
Finds the reward gained on the last iteration
"""
if len(opponent.history) == 0:
opp_prev_action = random_choice()
else:
opp_prev_action = opponent.history[-1]
return self.payoff_matrix[self.prev_action][opp_prev_action]
def find_reward(self, opponent):
"""
Finds the reward gained on the last iteration
"""
if len(opponent.history) == 0:
opp_prev_action = random_choice()
else:
opp_prev_action = opponent.history[-1]
return self.payoff_matrix[self.prev_action][opp_prev_action]
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 strategy(self, opponent):
current_round = len(self.history) + 1
if current_round == 1:
return C
elif current_round <= 20:
return opponent.history[-1]
else:
probability = 20 / current_round
return random_choice(probability)
def strategy(opponent):
# Cooperate on the first move
if not len(opponent.history):
return C
# Reciprocate cooperation
if opponent.history[-1] == C:
return C
# Respond to defections with probability equal to opponent's total
# proportion of defections
defection_prop = float(opponent.defections) / len(opponent.history)
return random_choice(1 - defection_prop)
def strategy(opponent):
# Cooperate on the first move
if not len(opponent.history):
return C
# Reciprocate cooperation
if opponent.history[-1] == C:
return C
# Respond to defections with probability equal to opponent's total
# proportion of defections
defection_prop = float(opponent.defections) / len(opponent.history)
return random_choice(1 - defection_prop)
def strategy(opponent):
"""Looks at opponent history to see if they have defected.
If so, player defection is inversely proportional to when this occurred.
"""
index = next((index for index, value in enumerate(opponent.history, start=1) if value == D), None)
if index is None:
return C
return random_choice(1 - 1 / float(abs(index)))
def strategy(opponent):
"""Looks at opponent history to see if they have defected.
If so, player defection is inversely proportional to when this occurred.
"""
index = next((index
for index, value in enumerate(opponent.history, start=1)
if value == D), None)
if index is None:
return C
return random_choice(1 - 1 / float(abs(index)))
def strategy(self, opponent):
"""Runs a qlearn algorithm while the tournament is running."""
state = self.find_state(opponent)
reward = self.find_reward(opponent)
if state not in self.Qs:
self.Qs[state] = OrderedDict(zip([C, D], [0, 0]))
self.Vs[state] = 0
self.perform_q_learning(self.prev_state, state, self.prev_action, reward)
if state not in self.Qs:
action = random_choice()
else:
action = self.select_action(state)
self.prev_state = state
self.prev_action = action
return action
def strategy(self, opponent):
"""Runs a qlearn algorithm while the tournament is running."""
state = self.find_state(opponent)
reward = self.find_reward(opponent)
if state not in self.Qs:
self.Qs[state] = OrderedDict(zip(['C', 'D'], [0, 0]))
self.Vs[state] = 0
self.perform_q_learning(self.prev_state, state, self.prev_action, reward)
if state not in self.Qs:
action = random_choice()
else:
action = self.select_action(state)
self.prev_state = state
self.prev_action = action
return action
def __init__(self):
"""Initialises the player by picking a random strategy."""
super(RiskyQLearner, self).__init__()
# Set this explicitely, since the constructor of super will not pick it up
# for any subclasses that do not override methods using random calls.
self.classifier['stochastic'] = True
self.prev_action = random_choice()
self.history = []
self.score = 0
self.Qs = OrderedDict({'': OrderedDict(zip([C, D], [0, 0])) })
self.Vs = OrderedDict({'': 0})
self.prev_state = ''
def __init__(self):
"""Initialises the player by picking a random strategy."""
super(RiskyQLearner, self).__init__()
# Set this explicitely, since the constructor of super will not pick it up
# for any subclasses that do not override methods using random calls.
self.classifier['stochastic'] = True
self.prev_action = random_choice()
self.history = []
self.score = 0
self.Qs = OrderedDict({'': OrderedDict(zip([C, D], [0, 0]))})
self.Vs = OrderedDict({'': 0})
self.prev_state = ''
class Geller(Player):
"""Observes what the player will do in the next round and adjust.
If unable to do this: will play randomly.
Geller - by Martin Chorley (@martinjc), heavily inspired by Matthew Williams (@voxmjw)
This code is inspired by Matthew Williams' talk
"Cheating at rock-paper-scissors — meta-programming in Python"
given at Django Weekend Cardiff in February 2014.
His code is here: https://github.com/mattjw/rps_metaprogramming
and there's some more info here: http://www.mattjw.net/2014/02/rps-metaprogramming/
This code is **way** simpler than Matt's, as in this exercise we already
have access to the opponent instance, so don't need to go
hunting for it in the stack. Instead we can just call it to
see what it's going to play, and return a result based on that
This is almost certainly cheating, and more than likely against the
spirit of the 'competition' :-)
"""
name = 'Geller'
default = lambda self: random_choice(0.5)
classifier = {
'memory_depth': -1,
'stochastic': True,
'makes_use_of': set(),
'long_run_time': False,
'inspects_source': True, # Finds out what opponent will do
'manipulates_source': False,
'manipulates_state': False
}
def strategy(self, opponent):
"""
Look at what the opponent will play in the next round and choose a strategy
that gives the least jail time, which is is equivalent to playing the same
strategy as that which the opponent will play.
"""
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
calname = calframe[1][3]
if calname == 'strategy':
return self.default()
else:
return opponent.strategy(self)
def __init__(self):
"""Initialises the player by picking a random strategy."""
super(RiskyQLearner, self).__init__()
# Set this explicitely, since the constructor of super will not pick it up
# for any subclasses that do not override methods using random calls.
self.stochastic = True
self.prev_action = random_choice()
self.history = []
self.score = 0
self.Qs = OrderedDict({'': OrderedDict(zip(['C', 'D'], [0, 0])) })
self.Vs = OrderedDict({'': 0})
self.prev_state = ''
(R, P, S, T) = Game().RPST()
self.payoff_matrix = {'C': {'C': R, 'D': S}, 'D': {'C': T, 'D': P}}
def strategy(self, opponent):
current_round = len(self.history) + 1
probability = 1 - float(current_round) / 1000
return random_choice(probability)
def strategy(opponent):
r = random.uniform(3, 7) / float(10)
return random_choice(r)
def strategy(self, opponent):
action = LookerUp.strategy(self, opponent)
# action could be 'C' or a float
if action in [C, D]:
return action
return random_choice(action)
def strategy(opponent):
if len(opponent.history) == 0:
return C
p = opponent.cooperations // len(opponent.history)
return random_choice(p)
def strategy(opponent):
if len(opponent.history) == 0:
return Actions.C
p = opponent.cooperations // len(opponent.history)
return random_choice(p)
def strategy(opponent):
if len(opponent.history) == 0:
# Randomly picks a strategy (not affected by history).
return random_choice(0.5)
p = opponent.cooperations // len(opponent.history)
return random_choice(p)
def strategy(self, opponent):
current_round = len(self.history) + 1
probability = current_round / 1000
return random_choice(probability)
def strategy(self, opponent):
current_round = len(self.history) + 1
expected_length = self.match_attributes["length"]
probability = 1 - float(current_round) / expected_length
return random_choice(probability)
def strategy(self, opponent):
current_round = len(self.history) + 1
expected_length = self.match_attributes['length']
probability = 1 - float(current_round) / expected_length
return random_choice(probability)
def strategy(opponent):
"""Randomly picks a strategy (not affected by history)."""
if len(opponent.history) == 0:
return 'C'
p = opponent.cooperations // len(opponent.history)
return random_choice(p)
def strategy(self, opponent):
if len(self.history) == 0:
return random_choice(0.6)
return self.history[-1]
def strategy(self, opponent):
if len(self.history) == 0:
return random_choice(0.6)
return self.history[-1]
def strategy(opponent):
if len(opponent.history) == 0:
# Randomly picks a strategy (not affected by history).
return random_choice(0.5)
p = opponent.cooperations // len(opponent.history)
return random_choice(p)
def strategy(self, opponent):
return random_choice(self.p)
def strategy(opponent):
r = random.uniform(3, 7) / float(10)
return random_choice(r)