def _action(self, state):
"""
Return best action given state and win/loss
Like predict process in NN
Best action: argmax( R(s, a) + gamma * max(R(s', a')) )
"""
try:
act = -1
rewards = self.eval(state)
for i in np.argsort(rewards[0, :].ravel())[::-1]:
if chkEmpty(state, i):
act = i
break
assert act != -1
return act
except:
print_exc()
set_trace()
def predict(self, state):
for i in xrange(6, -1, -1):
if chkEmpty(state, i):
return i
def rndAction(state):
try:
return choice([i for i in xrange(7) if chkEmpty(state, i)])
except:
print_exc()
set_trace()
