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random action improvement #1

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random action improvement #1

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11 changes: 8 additions & 3 deletions nim-varun/qLearningNim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,15 +177,20 @@ def getAction(self, state): # chooses an action from the given state based on ex
a = state[0]
b = state[1]
c = state[2]
ret = [-1, -1]
ret = [[-1, -1]]
max_val = -1
for i in range(0, 3): # otherwise, it will choose the action with the highest value, which exploits the existing strategy
for j in range(0, len(self.values[a][b][c][i])):
if max_val < self.values[a][b][c][i][j]:
max_val = self.values[a][b][c][i][j]
ret = [i, j]
ret = [[i, j]]
else max_val == self.values[a][b][c][i][j]:
ret.append([i, j])
#print("exploit {}".format(ret))
return ret


# randomly choose an action in ret
return ret[random.randint(0, len(ret) - 1)]

def updateValues(self, state, action, new_state, game_over, reward): # updates the q-table (learning happens here)
a = state[0]
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