def __call__(self, p1, p2):
self.temp = self.minTemperature
if self.useNetworks:
p1 = ModuleDecidingPlayer(p1, self.task.env, temperature = self.temp)
p2 = ModuleDecidingPlayer(p2, self.task.env, temperature = self.temp)
else:
assert isinstance(p1, CapturePlayer)
assert isinstance(p2, CapturePlayer)
p1.game = self.task.env
p2.game = self.task.env
p1.color = CaptureGame.BLACK
p2.color = -p1.color
self.player = p1
self.opponent = p2
# the games with increasing temperatures and lower coefficients
coeffSum = 0.
score = 0.
np = int(self.cases * (1-self.presetGamesProportion))
for i in range(self.maxGames):
coeff = 1/(10*self.temp+1)
preset = None
if self.cases > 1:
if i % self.cases >= np:
preset = self.sPos[(i-np) % self.cases]
elif i < self.cases:
# greedy, no need to repeat, just increase the coefficient
if i == 0:
coeff *= np
else:
continue
res = self._oneGame(preset)
score += coeff * res
coeffSum += coeff
if self.cases == 1 or (i % self.cases == 0 and i > 0):
self._globalWarming()
return score / coeffSum
def __call__(self, x):
""" If a module is given, wrap it into a ModuleDecidingAgent before evaluating it.
Also, if applicable, average the result over multiple games. """
if isinstance(x, Module):
agent = ModuleDecidingPlayer(x, self.env, greedySelection = True)
elif isinstance(x, CapturePlayer):
agent = x
else:
raise NotImplementedError('Missing implementation for '+x.__class__.__name__+' evaluation')
res = 0
agent.game = self.env
self.opponent.game = self.env
for dummy in range(self.averageOverGames):
agent.color = -self.opponent.color
x = EpisodicTask.__call__(self, agent)
res += x
return res / float(self.averageOverGames)
def __call__(self, x):
""" If a module is given, wrap it into a ModuleDecidingAgent before evaluating it.
Also, if applicable, average the result over multiple games. """
if isinstance(x, Module):
agent = ModuleDecidingPlayer(x, self.env, greedySelection=True)
elif isinstance(x, CapturePlayer):
agent = x
else:
raise NotImplementedError('Missing implementation for ' +
x.__class__.__name__ + ' evaluation')
res = 0
agent.game = self.env
self.opponent.game = self.env
for dummy in range(self.averageOverGames):
agent.color = -self.opponent.color
x = EpisodicTask.__call__(self, agent)
res += x
return res / float(self.averageOverGames)
