def test_greedy(self):
agent = Agent(1)
board = [1, 0, 0, 0, 0, -1, 0, 0, 0]
state1 = [[1, 0, 1, 0, 0, -1, 0, 0, 0], [0.5]]
state2 = [[1, 0, 0, 0, 1, -1, 0, 0, 0], [0.95]]
state3 = [[1, 1, 0, 0, 0, -1, 0, 0, 0], [0.5]]
state4 = [[1, 0, 0, 0, 0, -1, 0, 0, 1], [0.8]]
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
self.assertEqual(agent.greedy(board),
[[1, 0, 0, 0, 1, -1, 0, 0, 0], [0.95]])
agent = Agent(-1)
board = [1, 0, 0, 0, 0, -1, 0, 1, 0]
state1 = [[1, 0, 0, 0, 0, -1, 0, 1, -1], [0.5]]
state2 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.95]]
state3 = [[1, 0, 0, -1, 0, -1, 0, 1, 0], [0.75]]
state4 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
self.assertEqual(agent.greedy(board),
[[1, 0, 0, -1, 0, -1, 0, 1, 0], [0.75]])
agent = Agent(-1)
board = [1, 1, 0, 1, 0, -1, 0, -1, 0]
state1 = [[1, 1, 1, 1, 0, -1, 0, -1, -1], [0.5]]
state2 = [[1, 1, 0, 1, 0, -1, 1, -1, 0], [0.75]]
agent.add_state(state1)
agent.add_state(state2)
self.assertEqual(agent.greedy(board), [[], [0]])
def test_backprop_state_value(self):
state1 = [[1, 1, 0, 1, 1, -1, -1, -1, 0], [0.5]]
state2 = [[1, 1, 0, 1, 1, -1, -1, -1, 0], [0.7]]
agent = Agent(1)
agent.add_state(state1)
agent.add_state(state2)
board = [1, 1, 0, 1, 0, -1, -1, -1, 0]
agent.turn(board)
self.assertEqual(agent.pre_state[1], [0.7])
self.assertEqual(agent.current_state[1], [0.7])
def train():
stopwatch = Stopwatch()
stopwatch.start()
board = create_empty_board()
agent_x = Agent(1, LEARNING_RATE, AGENT_X_RANDOM_TURNS)
agent_o = Agent(-1, LEARNING_RATE, AGENT_O_RANDOM_TURNS)
for i in range(TRAINING_TURNS):
agent_x.turn(board)
if is_game_over(board):
board = create_empty_board()
agent_x.turn(board)
agent_x.reset_both_states()
agent_o.reset_both_states()
agent_o.turn(board)
if is_game_over(board):
board = create_empty_board()
agent_x.reset_both_states()
agent_o.reset_both_states()
if (i % 100) == 0:
print('Iteration= {} of {}.'.format(i, TRAINING_TURNS))
print('Training time= {}'.format(stopwatch.elapsed()))
return agent_x.states
def test_add_state(self):
state1 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.5]]
state2 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
state3 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.5]]
state4 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.8]]
state5 = [[0, 0, 0, 0, 0, -1, 0, 0, 0], [0.5]]
agent = Agent(1)
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
agent.add_state(state5)
self.assertEqual(len(agent.states), 5)
def test_turn(self):
agent = Agent(1)
board = [1, 0, 0, 1, 0, -1, 0, -1, 0]
state1 = [[1, 1, 0, 1, 0, -1, 0, -1, 0], [0.5]]
state2 = [[1, 1, 0, 1, 0, -1, 0, -1, 0], [0.75]]
state3 = [[1, 1, 0, 1, 0, -1, 0, -1, 0], [0.95]]
state4 = [[1, 1, 0, 1, 0, -1, 0, -1, 0], [0.8]]
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
self.assertEqual(agent.turn(board), [1, 1, 0, 1, 0, -1, 0, -1, 0])
def test_is_state_in_states(self):
agent = Agent(1)
state1 = [[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]]
state2 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
state3 = [[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]]
state4 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]
state5 = [[1, -1, 0, 0, 0, -1, 0, 1, 0], [0.8]]
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
self.assertEqual(agent.is_state_in_states(state3), True)
self.assertEqual(agent.is_state_in_states(state5), False)
def test_update_state(self):
agent = Agent(1)
state1 = [[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]]
state2 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
state3 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.5]]
state4 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
new_state_2 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
new_state_4 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.15]]
agent.update_state(new_state_2)
agent.update_state(new_state_4)
self.assertEqual(agent.states[1], new_state_2)
self.assertEqual(agent.states[3], new_state_4)
def test_next_states(self):
board = [1, 0, 0, 0, 0, -1, 0, 0, 0]
state1 = [[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]]
state2 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
state3 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.5]]
state4 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]
agent = Agent(1)
agent.add_state(state1)
agent.add_state(state2)
agent.add_state(state3)
agent.add_state(state4)
self.assertEqual(agent.next_states(board),
[[[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]],
[[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]])
board = [1, 0, 0, 1, 0, -1, 0, -1, 0]
state5 = [[1, 0, 0, 1, 0, -1, 0, 0, 0], [0.5]]
state6 = [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0.75]]
state7 = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0.5]]
state8 = [[1, 0, 0, 0, 0, -1, 0, 1, 0], [0.8]]
state9 = [[1, 0, 1, 1, 0, -1, 0, -1, 0], [0.8]]
state10 = [[1, 0, -1, 1, 0, -1, 0, -1, 0], [0.8]]
state11 = [[1, 0, 0, 1, 0, -1, 0, -1, 1], [0.9]]
agent.add_state(state5)
agent.add_state(state6)
agent.add_state(state7)
agent.add_state(state8)
agent.add_state(state9)
agent.add_state(state10)
agent.add_state(state11)
self.assertEqual(agent.next_states(board),
[[[1, 0, 1, 1, 0, -1, 0, -1, 0], [0.8]],
[[1, 0, 0, 1, 0, -1, 0, -1, 1], [0.9]]])
agent = Agent(-1)
board = [1, 1, 0, 1, 0, -1, 0, -1, 0]
state1 = [[1, 1, 0, 1, 0, -1, 0, -1, -1], [0.5]]
state2 = [[1, 1, 0, 1, 0, -1, 0, -1, 0], [0.75]]
agent.add_state(state1)
agent.add_state(state2)
self.assertEqual(agent.next_states(board),
[[[1, 1, 0, 1, 0, -1, 0, -1, -1], [0.5]]])
agent = Agent(-1)
board = [1, 1, 0, 1, 0, -1, 0, -1, 0]
state1 = [[1, 1, 1, 1, 0, -1, 0, -1, -1], [0.5]]
state2 = [[1, 1, 0, 1, 0, -1, 1, -1, 0], [0.75]]
agent.add_state(state1)
agent.add_state(state2)
self.assertEqual(agent.next_states(board), [])