def future_rewards(self, state):
"""
Calculates the maximum reward of state
Args:
state (nested list (3 x 3)): a Tictactoe board
Returns:
float: maximum reward
"""
# Get all the available ations
actions = Tictactoe.available_actions(state)
# Return 0 if not actions available
if len(actions) == 0:
return 0
# Choose the max reward according to the Q table
max_reward = self.get_q_value(state, actions[0])
for action in actions:
value = self.get_q_value(state, action)
if value > max_reward:
max_reward = value
return max_reward
def best_action(self, state, epsilon_true=False):
"""
Chooses the best action according to the state
Args:
state (nested list (3 x 3)): a Tictactoe board
Raises:
Exception: If no action is available
Returns:
tuple (i, j): best action
"""
# Get all the available ations
actions = Tictactoe.available_actions(state)
# Raise if no action available
if len(actions) == 0:
raise Exception('No action available')
# Choose the best action according to its Q value
if (not epsilon_true) or (epsilon_true and random.random() <
(1 - self.epsilon)):
best_action = actions[0]
max_reward = self.get_q_value(state, best_action)
for action in actions:
value = self.get_q_value(state, action)
if value > max_reward or (value == max_reward
and random.random() < 0.5):
best_action = action
max_reward = value
# Randomly choose the best action
else:
best_action = random.choice(actions)
return best_action
