def test_choose_optimal_move():
'''(5 points) choose_optimal_move()'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
p=MCTSPlayer()
#-------------------------
s=np.array([[ 1,-1, 1],
[ 0, 0,-1],
[ 0, 1,-1]])
n = MCNode(s, x=1)
n.build_tree(g,100)
r,c=p.choose_optimal_move(n)
assert r == 2
assert c == 0
#-------------------------
s=np.array([[ 1,-1, 1],
[ 0, 1,-1],
[ 0, 1,-1]])
n = MCNode(s, x=-1)
n.build_tree(g,100)
r,c=p.choose_optimal_move(n)
assert r == 2
assert c == 0
#-------------------------
s=np.array([[ 1,-1, 1],
[ 0, 0, 0],
[ 0, 0, 0]])
n = MCNode(s, x=-1)
n.build_tree(g,200)
r,c=p.choose_optimal_move(n)
assert r == 1
assert c == 1
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
s=np.array([[ 0,-1, 1,-1, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MCNode(s, x=1) # it's X player's turn
n.build_tree(g,100)
assert np.allclose(s,s_)
r,c=p.choose_optimal_move(n)
assert r == 0
assert c == 0
def A3C_train(shared_model: nn.Module, optimizer, counter, n):
model = Net(n)
model.train()
env = Othello(n)
env.play((4, 6))
state = env.data
max_episode_count = 10000
while counter.value < max_episode_count:
model.load_state_dict(shared_model.state_dict())
history = [] # (state, action, reward)
done = False
episode_length = 0
while not done and episode_length < n * n:
action = model.sample_action(
torch.from_numpy(state.astype(np.float32)).unsqueeze(0))
next_state, reward, done = env.step(action)
history.append((state, action, reward))
state = next_state
states, actions, rewards = zip(*history)
return_ = reward_to_return(rewards, 0.95, 0)
policy_logit, value = model(torch.Tensor(states))
td = torch.Tensor(return_) - value.squeeze(1)
policy_dist = [
logits_to_dist(pl)
for pl in policy_logit.view(policy_logit.size(0), -1)
]
advantage = td.detach()
value_loss = td.pow(2).mean()
policy_loss = (torch.cat([
-pd.log_prob(torch.from_numpy(np.where(a.flatten())[0]))
for pd, a in zip(policy_dist, actions)
]) * advantage).sum()
loss = value_loss + policy_loss
optimizer.zero_grad()
loss.backward()
# shared_modelの勾配として設定
for param, shared_param in zip(model.parameters(),
shared_model.parameters()):
if shared_param.grad is not None:
break
shared_param._grad = param.grad
optimizer.step()
# 終了条件を満たす場合、初期化
if done:
with counter.get_lock():
counter.value += 1
episode_length = 0
env = Othello(n)
state = env.data
class ReversiEnv(object):
"""docstring for ReversiEnv."""
def __init__(self):
super(ReversiEnv, self).__init__()
self.n = 8
def length(self):
return self.n * self.n
def reset(self):
self.game = Othello()
return self.game.get_state(), self.game.get_turn()
def action_space(self):
return self.game.get_actions()
def step(self, action):
self.game.move(action)
reward = self.game.get_winner()
return self.game.get_state(), self.game.get_turn(), reward
def test_choose_a_move():
'''(5 points) random choose_a_move()'''
# Game: TicTacToe
g = TicTacToe() # game
p = RandomPlayer()
s = np.array([[0, 1, 1], [1, 0, -1], [1, 1, 0]])
s_ = np.array([[0, 1, 1], [1, 0, -1], [1, 1, 0]])
count = np.zeros(3)
for _ in range(100):
r, c = p.choose_a_move(g, s, x=1)
assert s[r, c] == 0 # player needs to choose a valid move
assert np.allclose(
s, s_) # the player should never change the game state object
assert r == c # in this example the valid moves are on the diagonal of the matrix
assert r > -1 and r < 3
count[c] += 1
assert count[
0] > 20 # the random player should give roughly equal chance to each valid move
assert count[1] > 20
assert count[2] > 20
s = np.array([[1, 1, 0], [1, 0, -1], [0, 1, 1]])
for _ in range(100):
r, c = p.choose_a_move(g, s, x=1)
assert s[r, c] == 0
assert r == 2 - c
assert r > -1 and r < 3
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
g = Othello() # game
s = np.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, -1, -1, -1, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
p = RandomPlayer()
s_ = s.copy()
count = np.zeros(5)
for _ in range(200):
r, c = p.choose_a_move(g, s, x=1)
assert np.allclose(
s, s_) # the player should never change the game state object
assert s[r, c] == 0 # player needs to choose a valid move
assert r == 2
assert c > 1 and c < 7
count[c - 2] += 1
assert count[
0] > 20 # the random player should give roughly equal chance to each valid move
assert count[1] > 20
assert count[2] > 20
assert count[3] > 20
assert count[4] > 20
# test whether we can run a game using random player
s = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
for i in range(10):
e = g.run_a_game(p, p, s=s, x=1)
assert e == -1
s = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
w = 0
for i in range(10):
e = g.run_a_game(p, p, s=s, x=1)
w += e
assert np.abs(w) < 9
players = 0
size = 8
gui = True
verbose = True
if players == 2:
player1 = Player()
player2 = Player()
elif players == 1:
player1 = Player()
player2 = AI(greedy_move, 'Greedy AI')
else:
player1 = AI(greedy_move, 'Greedy AI')
player2 = AI(Negamax(4, score=EdgeScore(size)), 'Negamax AI')
game = Othello(player1, player2, size, verbose)
if gui:
size = 500
margin = 50
GUI(game, size, margin).mainloop()
elif players == 0:
games = 100
results = {player1: 0, player2: 0, GridGame.DRAW: 0}
for i in range(games):
game.play()
results[game.winner] += 1
game.reset()
print(results)
def run_a_game(p):
'''
Run a game
Input:
p: the AI player that you are playing with
'''
# initialize game state
g = Othello()
win = init_screen()
# initialize the game state
s = g.initial_game_state()
x = 1 # current turn (x player's turn)
# draw empty board
draw_empty_board(win,g,s,x)
canPlay = True
pygame.display.update()
# run the game
while True:
event = pygame.event.wait()
# close the window
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
# Press Key
if event.type == pygame.KEYDOWN:
# press F button (restart game)
if event.key == pygame.K_f:
s = g.initial_game_state()
x=1 # X player's turn
draw_empty_board(win,g,s,x)
canPlay = True
pygame.display.update()
# press ESC button (exit game)
if event.key == pygame.K_ESCAPE:
pygame.quit()
sys.exit()
# Click Mouse
if event.type is pygame.MOUSEBUTTONDOWN and canPlay and x==1:
# Human player's turn to choose a move
# get mouse position
(mouseX, mouseY) = pygame.mouse.get_pos()
# convert to board grid (row,column)
r, c = map_mouse_to_board(mouseX, mouseY)
# if the move is valid
if g.check_valid_move(s,r,c,x):
# update game state
x=g.apply_a_move(s,r,c,x)
# draw the board
draw_board(win,g,s,x)
# check if the game has ended already
e = g.check_game(s)
if e is not None:
draw_result(win,e)
canPlay = False
e=pygame.event.Event(pygame.USEREVENT)
pygame.event.post(e)
print("X player chooses:",str(r),str(c))
if event.type == pygame.USEREVENT and x== -1 and canPlay: # computer's turn to choose a move
r,c = p.choose_a_move(g,s,x)
# if the move is valid
assert g.check_valid_move(s,r,c,x)
# update game state
x=g.apply_a_move(s,r,c,x)
# draw the board
draw_board(win,g,s,x)
# check if the game has ended already
e = g.check_game(s)
if e is not None:
draw_result(win,e)
canPlay = False
e=pygame.event.Event(pygame.USEREVENT)
pygame.event.post(e)
print("O player chooses:",str(r),str(c))
# update the UI display
pygame.display.update()
def main():
mcts = MCTS()
game = Othello()
for i in range(32):
action = mcts.get_action(game)
game.move(action)
if game.game_over():
break
actions = game.get_actions()
probs = np.ones(actions.shape[0])
action = sample(probs, actions)
game.move(action)
if game.game_over():
break
# input('waiting')
print(game.get_true_state())
print(game.get_score())
print(game.get_winner())
print('game over')
def test_choose_optimal_move():
'''(5 points) choose_optimal_move()'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
p = MiniMaxPlayer()
#-------------------------
b = np.array([[1, -1, 1], [0, 0, -1], [0, 1, -1]])
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
n.compute_v(g)
r, c = p.choose_optimal_move(n)
assert r == 2
assert c == 0
#-------------------------
b = np.array([[1, -1, 1], [0, 1, -1], [0, 1, -1]])
s = GameState(b, x=-1) # it's O player's turn
n = MMNode(s)
n.build_tree(g)
n.compute_v(g)
r, c = p.choose_optimal_move(n)
assert r == 2
assert c == 0
#-------------------------
b = np.array([[1, -1, 1], [0, 0, 0], [0, 0, 0]])
s = GameState(b, x=-1) # it's O player's turn
n = MMNode(s)
n.build_tree(g)
n.compute_v(g)
r, c = p.choose_optimal_move(n)
assert r == 1
assert c == 1
#-------------------------
b = np.array([[1, -1, 1], [0, 1, -1], [-1, 1, -1]])
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
n.compute_v(g)
r, c = p.choose_optimal_move(n)
assert r == 1
assert c == 0
# The AI agent should also be compatible with Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
n.compute_v(g)
assert np.allclose(n.s.b, b_)
r, c = p.choose_optimal_move(n)
assert r == 0
assert c == 0
def test_compute_v():
'''(5 points) compute_v()'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
#-------------------------
# the value of a terminal node is its game result
s = np.array([[1, 0, 0], [0, 1, -1], [0, -1, 1]])
n = MMNode(s, x=-1)
n.compute_v(g)
assert n.v == 1 # X player won the game
# the value of a terminal node is its game result
s = np.array([[1, 1, -1], [-1, 1, 1], [1, -1, -1]])
n = MMNode(s, x=-1)
n.compute_v(g)
assert n.v == 0 # A tie
# the value of a terminal node is its game result
s = np.array([[1, 0, 1], [0, 0, 1], [-1, -1, -1]])
n = MMNode(s, x=1)
n.compute_v(g)
assert n.v == -1 # O player won the game
#-------------------------
# if it is X player's turn, the value of the current node is the max value of all its children nodes.
s = np.array([[0, -1, 1], [0, 1, -1], [0, -1, 1]])
n = MMNode(s, x=1)
n.build_tree(g)
# the current node has 3 children nodes, two of which are terminal nodes (X player wins)
n.compute_v(g)
# so the max value among the three children nodes max(1,?,1) = 1 (here ? is either 1 or 0 or -1)
assert n.v == 1 # X player won the game
#-------------------------
# if it is O player's turn, the value of the current node is the min value of all its children nodes.
s = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
n = MMNode(s, x=-1)
n.build_tree(g)
# the current node has 2 children nodes, one of them is a terminal node (O player wins)
n.compute_v(g)
# so the min value among the two children nodes min(-1,0) =-1
assert n.v == -1 # O player won the game
#-------------------------
# a tie after one move
s = np.array([[-1, 1, -1], [-1, 1, 1], [0, -1, 1]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 0
#-------------------------
# optimal moves lead to: O player wins
s = np.array([[-1, 1, -1], [1, 0, 0], [1, 0, 0]])
n = MMNode(s, x=-1)
n.build_tree(g)
n.compute_v(g)
assert n.v == -1
#-------------------------
# optimal moves lead to a tie
s = np.array([[0, 1, 0], [0, 1, 0], [0, 0, -1]])
n = MMNode(s, x=-1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 0
#-------------------------
# optimal moves lead to: X player wins
s = np.array([[1, -1, 1], [0, 0, 0], [0, -1, 0]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [0, 0, 0], [0, 0, -1]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [0, 0, -1], [0, 0, 0]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [-1, 0, 0], [0, 0, 0]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [0, 0, 0], [-1, 0, 0]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [0, 0, 1], [0, 0, -1]])
n = MMNode(s, x=-1)
n.build_tree(g)
n.compute_v(g)
assert n.v == -1
s = np.array([[1, -1, 1], [0, 0, -1], [0, 1, -1]])
n = MMNode(s, x=1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[1, -1, 1], [0, 0, 0], [0, 1, -1]])
n = MMNode(s, x=-1)
n.build_tree(g)
n.compute_v(g)
assert n.v == 0
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
s = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
n.compute_v(g)
assert np.allclose(s, s_)
assert n.v == 1
s = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
n.compute_v(g)
assert n.v == -1
s = np.array([[0, 0, -1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
n = MMNode(s, x=-1) # it's O player's turn
n.build_tree(g)
n.compute_v(g)
assert n.v == -1
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
s = np.array([[0, -1, 1, -1, 1, -1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0], [-1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
n.compute_v(g)
assert n.v == 1
def test_expand():
'''(5 points) expand'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# Current Node (root)
b = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
s = GameState(b, x=1) #it's X player's turn
n = MCNode(s)
# expand
sc = n.expand(g)
assert n.s.x == 1
assert len(n.c) == 3
assert type(sc) == MCNode
assert sc.p == n
assert sc.s.x == -1
assert sc.p == n
assert sc.c == []
assert sc.v == 0
assert sc.N == 0
b_ = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert c.s.x == -1
assert c.p == n
assert c.c == []
assert c.v == 0
assert c.N == 0
# child node A
b = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert c
# child node B
b = np.array([[0, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert c
# child node C
b = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert c
# the selected child node should be in the children list
c = False
for x in n.c:
if sc == x:
c = True
assert c
#--------------------------
# Current Node (root)
b = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
s = GameState(b, x=-1) #it's O player's turn
n = MCNode(s)
sc = n.expand(g)
assert n.s.x == -1
assert len(n.c) == 2
assert type(sc) == MCNode
assert sc.p == n
assert sc.s.x == 1
assert sc.p == n
assert sc.c == []
assert sc.v == 0
assert sc.N == 0
for c in n.c:
assert c.s.x == 1
assert c.p == n
assert c.c == []
assert c.v == 0
assert c.N == 0
# child node A
b = np.array([[1, 1, -1], [-1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert c
# child node B
b = np.array([[1, 1, -1], [0, -1, 1], [-1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert c
# the selected child node should be in the children list
c = False
for x in n.c:
if sc == x:
c = True
assert c
#---------------------------
s = GameState(np.zeros((3, 3)), x=1)
n = MCNode(s)
sc = n.expand(g)
assert n.s.x == 1
assert len(n.c) == 9
a = False
for c in n.c:
assert c.s.x == -1
assert c.p == n
assert c.c == []
assert np.sum(c.s.b) == 1
assert c.v == 0
assert c.N == 0
if sc == c:
a = True
assert a # the selected child node should be in the children list
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=1) #it's X player's turn
n = MCNode(s)
# expand
n.expand(g)
assert len(n.c) == 2
assert n.s.x == 1
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MCNode
assert c.p == n
assert c.c == []
assert c.v == 0
# child node A
b = np.array([[1, 1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.s.x == 1 # it is still X player's turn because there is no valid move for O player
assert c
# child node B
b = np.array([[0, -1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 4)
assert x.s.x == -1
assert c
#---------------------
b = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=-1) #it's O player's turn
n = MCNode(s)
# expand
n.expand(g)
print(n.c)
assert len(n.c) == 3
assert n.s.x == -1
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MCNode
assert c.p == n
assert c.c == []
assert c.v == 0
# child node A
b = np.array([[-1, -1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.s.x == -1 # no valid move for X player
assert c
# child node B
b = np.array([[0, 1, -1, -1, -1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 4)
assert x.s.x == 1
assert c
# child node C
b = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0],
[0, 0, -1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 2)
assert x.s.x == 1
assert c
def reset(self):
self.game = Othello()
return self.game.get_state(), self.game.get_turn()
def test_build_tree():
'''(5 points) build_tree'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# current node (root node)
b = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
b_ = b.copy()
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(b, b_)
assert len(n.c) == 3
assert n.s.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MMNode
assert c.s.x == -1
assert c.p == n
assert len(c.c) == 2
assert c.v == None
#-----------------------
# child node A
b = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
ca = x
assert c
# child node B
b = np.array([[0, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
cb = x
assert c
# child node C
b = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
cc = x
assert c
#-----------------------
# Child Node A's children
for c in ca.c:
assert c.s.x == 1
assert c.p == ca
assert c.v == None
# grand child node A1
b = np.array([[1, 1, -1], [-1, -1, 1], [0, 1, -1]])
c = False
for x in ca.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert len(x.c) == 1
#-----------------------
# Great Grand Child Node A11
assert x.c[0].s.x == -1
assert x.c[0].p == x
assert x.c[0].v == None
assert x.c[0].c == []
assert c
# grand child node A2
b = np.array([[1, 1, -1], [0, -1, 1], [-1, 1, -1]])
c = False
for x in ca.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert x.c == []
assert c
#-----------------------
# Child Node B's children
for c in cb.c:
assert c.s.x == 1
assert c.p == cb
assert c.c == []
assert c.v == None
# grand child node B1
b = np.array([[-1, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in cb.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert c
# grand child node B2
b = np.array([[0, 1, -1], [1, -1, 1], [-1, 1, -1]])
c = False
for x in cb.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert c
#-----------------------
# Child Node C's children
for c in cc.c:
assert c.s.x == 1
assert c.p == cc
assert c.v == None
# grand child node C1
b = np.array([[-1, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in cc.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.c == []
assert c
# grand child node C2
b = np.array([[0, 1, -1], [-1, -1, 1], [1, 1, -1]])
c = False
for x in cc.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert len(x.c) == 1
# Great Grand Child Node C21
assert x.c[0].s.x == -1
assert x.c[0].p == x
assert x.c[0].v == None
assert x.c[0].c == []
assert c
#-----------------------
b = np.array([[0, 0, 1], [0, 1, 1], [-1, 0, -1]])
s = GameState(b, x=-1) #it's O player's turn
n = MMNode(s)
n.build_tree(g)
assert len(n.c) == 4
assert n.s.x == -1
assert n.v == None
assert n.p == None
assert n.m == None
b1 = np.array([[-1, 0, 1], [0, 1, 1], [-1, 0, -1]])
b2 = np.array([[0, -1, 1], [0, 1, 1], [-1, 0, -1]])
b3 = np.array([[0, 0, 1], [-1, 1, 1], [-1, 0, -1]])
b4 = np.array([[0, 0, 1], [0, 1, 1], [-1, -1, -1]])
for c in n.c:
assert c.s.x == 1
assert c.v == None
assert c.p == n
if np.allclose(c.s.b, b1):
assert c.m == (0, 0)
assert len(c.c) == 3
if np.allclose(c.s.b, b2):
assert c.m == (0, 1)
assert len(c.c) == 3
if np.allclose(c.s.b, b3):
assert c.m == (1, 0)
assert len(c.c) == 3
if np.allclose(c.s.b, b4):
assert c.m == (2, 1)
assert c.c == [] #terminal node, no child
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
b = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(n.s.b, b_)
assert len(n.c) == 2
assert n.s.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
for c in n.c:
assert type(c) == MMNode
assert c.s.x == -1
assert c.p == n
assert c.v == None
assert len(c.c) == 1
#-----------------------
# child node A
b = np.array([[0, 0, -1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 5)
ca = x
assert c
#-----------------------
# child node B
b = np.array([[0, 1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 1)
cb = x
assert c
#-----------------------
# Child Node A's children
# grand child node A1
assert ca.c[0].p == ca
assert ca.c[0].v == None
assert ca.c[0].m == (0, 6)
assert ca.c[0].c == []
b = np.array([[0, 0, -1, -1, -1, -1, -1, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(ca.c[0].s.b, b)
#-----------------------
# Child Node B's children
# grand child node B1
assert cb.c[0].p == cb
assert cb.c[0].v == None
assert cb.c[0].m == (0, 0)
assert cb.c[0].c == []
b = np.array([[-1, -1, -1, -1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(cb.c[0].s.b, b)
#------------------------------------
b = np.array([[0, -1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=1) # it's X player's turn
n = MMNode(s)
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(n.s.b, b_)
assert len(n.c) == 2
assert n.s.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.v == None
assert len(c.c) == 1
#-----------------------
# child node A
b = np.array([[1, 1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.s.x == 1 # there is no valid move for O player, so O player needs to give up the chance
ca = x
assert c
#-----------------------
# child node B
b = np.array([[0, -1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 5)
assert x.s.x == -1
cb = x
assert c
#-----------------------
# Child Node A's children
# grand child node A1
assert ca.c[0].p == ca
assert ca.c[0].v == None
assert ca.c[0].m == (0, 5)
assert ca.c[0].c == []
b = np.array([[1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(ca.c[0].s.b, b)
#-----------------------
# Child Node B's children
# grand child node B1
assert cb.c[0].p == cb
assert cb.c[0].v == None
assert cb.c[0].m == (0, 6)
assert cb.c[0].c == []
b = np.array([[0, -1, -1, -1, -1, -1, -1, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(cb.c[0].s.b, b)
#---------------------
# The AI agent should also be compatible with the game: GO
# now let's test on the game "GO":
g = GO(board_size=2) # game (2 x 2 board)
b = np.array([[1, 1], [1, 0]])
s = GO_state(b, x=1, a=1) #it's X player's turn
n = MMNode(s)
n.build_tree(g)
assert len(n.c) == 1
assert n.c[0].s.x == -1
assert n.c[0].s.a == 2
assert len(n.c[0].c) == 0
l = np.array([[0, 0], [0, -1]])
p = {np.array2string(l)}
s = GO_state(b, x=-1, p=p, a=1) #it's O player's turn
n = MMNode(s)
n.build_tree(g)
assert len(n.c) == 1
assert n.c[0].s.x == 1
assert n.c[0].s.a == 2
assert len(n.c[0].c) == 0
g = GO(board_size=2, max_game_length=1) # game (2 x 2 board)
b = np.array([[1, 1], [1, 0]])
s = GO_state(b, x=-1) #it's X player's turn
n = MMNode(s)
n.build_tree(g)
assert len(n.c) == 2
assert n.c[0].s.x == 1
assert len(n.c[0].c) == 0
g = GO(board_size=2, max_game_length=2) # game (2 x 2 board)
b = np.array([[1, 1], [1, 0]])
s = GO_state(b, x=-1) #it's X player's turn
n = MMNode(s)
n.build_tree(g)
assert len(n.c) == 2
for c in n.c:
assert c.s.x == 1
if np.allclose(c.s.b, b):
assert len(c.c) == 1
else:
assert len(c.c) == 4
def test_expand():
'''(5 points) expand'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# Current Node (root)
b = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
s = GameState(b, x=1) #it's X player's turn
n = MMNode(s)
# expand
n.expand(g)
assert len(n.c) == 3
assert n.s.x == 1
b_ = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MMNode
assert c.s.x == -1
assert c.p == n
assert c.c == [
] #only add one level of children nodes, not two levels.
assert c.v == None
# child node A
b = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert c
# child node B
b = np.array([[0, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert c
# child node C
b = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert c
#--------------------------
# Current Node (root)
b = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
s = GameState(b, x=-1) #it's O player's turn
n = MMNode(s)
n.expand(g)
assert n.s.x == -1
assert len(n.c) == 2
for c in n.c:
assert c.s.x == 1
assert c.p == n
assert c.c == []
# child node A
b = np.array([[1, 1, -1], [-1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (1, 0)
assert c
# child node B
b = np.array([[1, 1, -1], [0, -1, 1], [-1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 0)
assert c
#---------------------------
s = GameState(np.zeros((3, 3)), x=1) #it's X player's turn
n = MMNode(s)
n.expand(g)
assert n.s.x == 1
assert len(n.c) == 9
for c in n.c:
assert c.s.x == -1
assert c.p == n
assert c.c == []
assert np.sum(c.s.b) == 1
assert c.v == None
#---------------------
# The AI agent should also be compatible with Othello game.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=1) #it's X player's turn
n = MMNode(s)
# expand
n.expand(g)
assert len(n.c) == 2
assert n.s.x == 1
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.c == []
assert c.v == None
# child node A
b = np.array([[1, 1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.s.x == 1 # it is still X player's turn because there is no valid move for O player
assert c
# child node B
b = np.array([[0, -1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 4)
assert x.s.x == -1
assert c
#---------------------
b = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
s = GameState(b, x=-1) #it's O player's turn
n = MMNode(s)
# expand
n.expand(g)
print(n.c)
assert len(n.c) == 3
assert n.s.x == -1
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.c == []
assert c.v == None
# child node A
b = np.array([[-1, -1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 0)
assert x.s.x == -1 # no valid move for X player
assert c
# child node B
b = np.array([[0, 1, -1, -1, -1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (0, 4)
assert x.s.x == 1
assert c
# child node C
b = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0],
[0, 0, -1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s.b, b):
c = True
assert x.m == (2, 2)
assert x.s.x == 1
assert c
#---------------------
# The AI agent should also be compatible with the game: GO
# now let's test on the game "GO":
g = GO(board_size=2) # game (2 x 2 board)
b = np.array([[0, 1], [1, 0]])
b_ = b.copy()
s = GO_state(b, x=-1) #it's O player's turn
n = MMNode(s)
# expand
n.expand(g)
assert len(n.c) == 1 # only one valid move for O player: 'pass'
assert n.s.x == -1
# the current game state should not change after expanding
assert np.allclose(n.s.b, b_)
c = n.c[0]
assert type(c) == MMNode
assert c.p == n
assert c.c == []
assert c.v == None
assert np.allclose(c.s.b, b_)
assert c.m[0] is None
assert c.m[1] is None
s = GO_state(b, x=1) #it's X player's turn
n = MMNode(s)
# expand
n.expand(g)
assert len(n.c) == 3
def test_choose_a_move():
'''(5 points) random choose_a_move()'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
p = RandomPlayer()
b = np.array([[0, 1, 1], [1, 0, -1], [1, 1, 0]])
b_ = np.array([[0, 1, 1], [1, 0, -1], [1, 1, 0]])
s = GameState(b, x=1)
count = np.zeros(3)
for _ in range(100):
r, c = p.choose_a_move(g, s)
assert b_[r, c] == 0 # player needs to choose a valid move
assert np.allclose(
s.b, b_) # the player should never change the game state object
assert r == c # in this example the valid moves are on the diagonal of the matrix
assert r > -1 and r < 3
count[c] += 1
assert count[
0] > 20 # the random player should give roughly equal chance to each valid move
assert count[1] > 20
assert count[2] > 20
b = np.array([[1, 1, 0], [1, 0, -1], [0, 1, 1]])
s = GameState(b, x=1)
for _ in range(100):
r, c = p.choose_a_move(g, s)
assert b[r, c] == 0
assert r == 2 - c
assert r > -1 and r < 3
#---------------------
# The AI agent should also be compatible with the game Othello.
# now let's test on the game "Othello":
g = Othello() # game
b = np.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, -1, -1, -1, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
b_ = b.copy()
p = RandomPlayer()
s = GameState(b, x=1)
count = np.zeros(5)
for _ in range(200):
r, c = p.choose_a_move(g, s)
assert np.allclose(
b, b_) # the player should never change the game state object
assert b[r, c] == 0 # player needs to choose a valid move
assert r == 2
assert c > 1 and c < 7
count[c - 2] += 1
assert count[
0] > 20 # the random player should give roughly equal chance to each valid move
assert count[1] > 20
assert count[2] > 20
assert count[3] > 20
assert count[4] > 20
# test whether we can run a game using random player
b = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s = GameState(b, x=1)
for i in range(10):
e = g.run_a_game(p, p, s=s)
assert e == -1
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s = GameState(b, x=1)
w = 0
for i in range(10):
e = g.run_a_game(p, p, s=s)
w += e
assert np.abs(w) < 9
# test whether we can run a game using random player
b = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s = GameState(b, x=1)
for i in range(10):
e = g.run_a_game(p, p, s=s)
assert e == -1
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
w = 0
s = GameState(b, x=1)
for i in range(10):
e = g.run_a_game(p, p, s=s)
w += e
assert np.abs(w) < 9
#---------------------
# The AI agent should also be compatible with the game: GO
# now let's test on the game "GO":
g = GO(board_size=2) # game (2 x 2 board)
s = g.initial_game_state()
p = RandomPlayer()
b_ = s.b.copy()
count = np.zeros(5)
for _ in range(200):
r, c = p.choose_a_move(g, s)
assert np.allclose(
s.b, b_) # the player should never change the game state object
assert s.a == 0
if r is None and c is None: # the player choose to pass without placing any stone in the step
count[-1] += 1
else:
count[2 * r + c] += 1
assert count[
0] > 20 # the random player should give roughly equal chance to each valid move
assert count[1] > 20
assert count[2] > 20
assert count[3] > 20
assert count[4] > 20
def test_expand():
'''(5 points) expand'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# Current Node (root)
s = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
n = MMNode(s, x=1) #it's X player's turn
# expand
n.expand(g)
assert len(n.c) == 3
assert n.x == 1
s_ = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
# the current game state should not change after expanding
assert np.allclose(n.s, s_)
for c in n.c:
assert type(c) == MMNode
assert c.x == -1
assert c.p == n
assert c.c == [
] #only add one level of children nodes, not two levels.
assert c.v == None
# child node A
s = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert c
# child node B
s = np.array([[0, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (1, 0)
assert c
# child node C
s = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 0)
assert c
#--------------------------
# Current Node (root)
s = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
n = MMNode(s, -1) #it's O player's turn
n.expand(g)
assert n.x == -1
assert len(n.c) == 2
for c in n.c:
assert c.x == 1
assert c.p == n
assert c.c == []
# child node A
s = np.array([[1, 1, -1], [-1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (1, 0)
assert c
# child node B
s = np.array([[1, 1, -1], [0, -1, 1], [-1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 0)
assert c
#---------------------------
n = MMNode(np.zeros((3, 3)), 1)
n.expand(g)
assert n.x == 1
assert len(n.c) == 9
for c in n.c:
assert c.x == -1
assert c.p == n
assert c.c == []
assert np.sum(c.s) == 1
assert c.v == None
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
s = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MMNode(s, x=1) #it's X player's turn
# expand
n.expand(g)
assert len(n.c) == 2
assert n.x == 1
# the current game state should not change after expanding
assert np.allclose(n.s, s_)
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.c == []
assert c.v == None
# child node A
s = np.array([[1, 1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert x.x == 1 # it is still X player's turn because there is no valid move for O player
assert c
# child node B
s = np.array([[0, -1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 4)
assert x.x == -1
assert c
#---------------------
s = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MMNode(s, x=-1) #it's O player's turn
# expand
n.expand(g)
print(n.c)
assert len(n.c) == 3
assert n.x == -1
# the current game state should not change after expanding
assert np.allclose(n.s, s_)
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.c == []
assert c.v == None
# child node A
s = np.array([[-1, -1, -1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert x.x == -1 # no valid move for X player
assert c
# child node B
s = np.array([[0, 1, -1, -1, -1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 4)
assert x.x == 1
assert c
# child node C
s = np.array([[0, 1, -1, 1, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0],
[0, 0, -1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 2)
assert x.x == 1
assert c
def test_build_tree():
'''(5 points) build_tree'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# current node (root node)
s = np.array([[0, 1, -1], [0, -1, 1], [0, 1, -1]])
s_ = s.copy()
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(s, s_)
assert len(n.c) == 3
assert n.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
assert np.allclose(n.s, s_)
for c in n.c:
assert type(c) == MMNode
assert c.x == -1
assert c.p == n
assert len(c.c) == 2
assert c.v == None
#-----------------------
# child node A
s = np.array([[1, 1, -1], [0, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
ca = x
assert c
# child node B
s = np.array([[0, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (1, 0)
cb = x
assert c
# child node C
s = np.array([[0, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 0)
cc = x
assert c
#-----------------------
# Child Node A's children
for c in ca.c:
assert c.x == 1
assert c.p == ca
assert c.v == None
# grand child node A1
s = np.array([[1, 1, -1], [-1, -1, 1], [0, 1, -1]])
c = False
for x in ca.c:
if np.allclose(x.s, s):
c = True
assert x.m == (1, 0)
assert len(x.c) == 1
#-----------------------
# Great Grand Child Node A11
assert x.c[0].x == -1
assert x.c[0].p == x
assert x.c[0].v == None
assert x.c[0].c == []
assert c
# grand child node A2
s = np.array([[1, 1, -1], [0, -1, 1], [-1, 1, -1]])
c = False
for x in ca.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 0)
assert x.c == []
assert c
#-----------------------
# Child Node B's children
for c in cb.c:
assert c.x == 1
assert c.p == cb
assert c.c == []
assert c.v == None
# grand child node B1
s = np.array([[-1, 1, -1], [1, -1, 1], [0, 1, -1]])
c = False
for x in cb.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert c
# grand child node B2
s = np.array([[0, 1, -1], [1, -1, 1], [-1, 1, -1]])
c = False
for x in cb.c:
if np.allclose(x.s, s):
c = True
assert x.m == (2, 0)
assert c
#-----------------------
# Child Node C's children
for c in cc.c:
assert c.x == 1
assert c.p == cc
assert c.v == None
# grand child node C1
s = np.array([[-1, 1, -1], [0, -1, 1], [1, 1, -1]])
c = False
for x in cc.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert x.c == []
assert c
# grand child node C2
s = np.array([[0, 1, -1], [-1, -1, 1], [1, 1, -1]])
c = False
for x in cc.c:
if np.allclose(x.s, s):
c = True
assert x.m == (1, 0)
assert len(x.c) == 1
# Great Grand Child Node C21
assert x.c[0].x == -1
assert x.c[0].p == x
assert x.c[0].v == None
assert x.c[0].c == []
assert c
#-----------------------
s = np.array([[0, 0, 1], [0, 1, 1], [-1, 0, -1]])
n = MMNode(s, x=-1) #it's O player's turn
n.build_tree(g)
assert len(n.c) == 4
assert n.x == -1
assert n.v == None
assert n.p == None
assert n.m == None
s1 = np.array([[-1, 0, 1], [0, 1, 1], [-1, 0, -1]])
s2 = np.array([[0, -1, 1], [0, 1, 1], [-1, 0, -1]])
s3 = np.array([[0, 0, 1], [-1, 1, 1], [-1, 0, -1]])
s4 = np.array([[0, 0, 1], [0, 1, 1], [-1, -1, -1]])
for c in n.c:
assert c.x == 1
assert c.v == None
assert c.p == n
if np.allclose(c.s, s1):
assert c.m == (0, 0)
assert len(c.c) == 3
if np.allclose(c.s, s2):
assert c.m == (0, 1)
assert len(c.c) == 3
if np.allclose(c.s, s3):
assert c.m == (1, 0)
assert len(c.c) == 3
if np.allclose(c.s, s4):
assert c.m == (2, 1)
assert c.c == [] #terminal node, no child
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
s = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(s, s_)
assert len(n.c) == 2
assert n.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
for c in n.c:
assert type(c) == MMNode
assert c.x == -1
assert c.p == n
assert c.v == None
assert len(c.c) == 1
#-----------------------
# child node A
s = np.array([[0, 0, -1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 5)
ca = x
assert c
#-----------------------
# child node B
s = np.array([[0, 1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 1)
cb = x
assert c
#-----------------------
# Child Node A's children
# grand child node A1
assert ca.c[0].p == ca
assert ca.c[0].v == None
assert ca.c[0].m == (0, 6)
assert ca.c[0].c == []
s = np.array([[0, 0, -1, -1, -1, -1, -1, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(ca.c[0].s, s)
#-----------------------
# Child Node B's children
# grand child node B1
assert cb.c[0].p == cb
assert cb.c[0].v == None
assert cb.c[0].m == (0, 0)
assert cb.c[0].c == []
s = np.array([[-1, -1, -1, -1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(cb.c[0].s, s)
#------------------------------------
s = np.array([[0, -1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s_ = s.copy()
n = MMNode(s, x=1) # it's X player's turn
n.build_tree(g)
# the current game state should not change after building the tree
assert np.allclose(s, s_)
assert len(n.c) == 2
assert n.x == 1
assert n.v == None
assert n.p == None
assert n.m == None
for c in n.c:
assert type(c) == MMNode
assert c.p == n
assert c.v == None
assert len(c.c) == 1
#-----------------------
# child node A
s = np.array([[1, 1, 1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 0)
assert x.x == 1 # there is no valid move for O player, so O player needs to give up the chance
ca = x
assert c
#-----------------------
# child node B
s = np.array([[0, -1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
c = False
for x in n.c:
if np.allclose(x.s, s):
c = True
assert x.m == (0, 5)
assert x.x == -1
cb = x
assert c
#-----------------------
# Child Node A's children
# grand child node A1
assert ca.c[0].p == ca
assert ca.c[0].v == None
assert ca.c[0].m == (0, 5)
assert ca.c[0].c == []
s = np.array([[1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(ca.c[0].s, s)
#-----------------------
# Child Node B's children
# grand child node B1
assert cb.c[0].p == cb
assert cb.c[0].v == None
assert cb.c[0].m == (0, 6)
assert cb.c[0].c == []
s = np.array([[0, -1, -1, -1, -1, -1, -1, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
assert np.allclose(cb.c[0].s, s)
def test_sample():
'''(5 points) sample'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
#------------------------
b = np.array([[0, 1, 1], [0, -1, 1], [-1, 1, -1]])
bc = np.array([[0, 1, 1], [0, -1, 1], [-1, 1, -1]])
s = GameState(b, x=-1) # "O" player's turn
n = MCNode(s)
assert np.allclose(n.s.b,
bc) # the game state should not change after simulation
v = 0
for _ in range(100):
e = n.sample(g)
assert e == -1 or e == 1
v += e
assert np.abs(
v) < 25 # the two results should have roughly the same chance
#------------------------
b = np.array([[0, 1, 1], [-1, -1, 1], [-1, 1, -1]])
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
for _ in range(100):
e = n.sample(g)
assert e == 1
#------------------------
b = np.array([[0, 1, 0], [-1, -1, 1], [-1, 1, 1]])
s = GameState(b, x=-1) # "O" player's turn
n = MCNode(s)
for _ in range(100):
e = n.sample(g)
assert e == -1
#------------------------
b = np.array([[0, 1, 1], [0, -1, 1], [0, -1, -1]])
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
v = 0
for _ in range(100):
e = n.sample(g)
assert e == -1 or e == 1
v += e
assert np.abs(v) < 25 # X player has 1/2 chance to win and 1/2 to lose
#------------------------
# Terminal node, the game has already ended, the simulation result should always be the same.
b = np.array([[-1, 0, 0], [1, -1, 1], [0, 1,
-1]]) # terminal node: O player won
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
for _ in range(100):
assert n.sample(g) == -1
b_ = np.array([[-1, 0, 0], [1, -1, 1], [0, 1, -1]])
assert np.allclose(n.s.b,
b_) # the game state should not change after simulation
b = np.array([[-1, -1, 1], [1, 1, -1], [-1, 1, 1]])
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
for _ in range(100):
assert n.sample(g) == 0
#------------------------
b = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
s = GameState(b, x=-1) # "O" player's turn
n = MCNode(s)
v = 0
for _ in range(1000):
e = n.sample(g)
assert e == -1 or e == 1 or e == 0
v += e
assert np.abs(v - 500) < 100
#-----------------------------
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
#------------------------
b = np.array([[0, 0, -1, 1, -1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
for _ in range(10):
e = n.sample(g)
assert e == -1
#------------------------
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
s = GameState(b, x=1) # "X" player's turn
n = MCNode(s)
w = 0
for _ in range(10):
e = n.sample(g)
w += e
assert np.abs(
w
) < 9 # the two results (1 and -1) should roughly have the same chance
def test_minmax_choose_a_move():
'''(10 points) minmax choose_a_move()'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
# two possible moves: one leads to win
p = MiniMaxPlayer()
s = np.array([[0, -1, 1], [-1, 1, 1], [0, 1, -1]])
s_ = s.copy()
r, c = p.choose_a_move(g, s, x=1)
assert np.allclose(s, s_)
assert r == 2
assert c == 0
# three possible moves, one leads to win
p = MiniMaxPlayer()
s = np.array([[1, -1, 1], [0, 0, -1], [0, 1, -1]])
r, c = p.choose_a_move(g, s, x=1)
assert r == 2
assert c == 0
#-------------------------
p = MiniMaxPlayer()
s = np.array([[1, -1, 1], [0, 0, 0], [0, 0, 0]])
r, c = p.choose_a_move(g, s, x=-1) # O player's turn
assert r == 1
assert c == 1
#-------------------------
# play against random player in the game
p1 = MiniMaxPlayer()
p2 = RandomPlayer()
# X Player: MinMax
# O Player: Random
s = np.array([[1, -1, 1], [0, 0, 0], [0, 0, -1]])
for i in range(10):
e = g.run_a_game(p1, p2, s=s, x=1)
assert e == 1
#-------------------------
# play against MinMax player in the game
# X Player: MinMax
# O Player: MinMax
s = np.array([[1, -1, 1], [0, 0, -1], [0, 1, -1]])
for i in range(10):
e = g.run_a_game(p1, p1, s=s, x=1)
assert e == 1
s = np.array([[0, 0, 1], [0, -1, 0], [1, -1, 0]])
e = g.run_a_game(p1, p1, s=s)
assert e == 0
s = np.array([[0, 0, 0], [0, -1, 0], [1, 0, 0]])
e = g.run_a_game(p1, p1, s=s)
assert e == 0
s = np.array([[0, 0, 0], [0, 0, 0], [1, -1, 0]])
e = g.run_a_game(p1, p1, s=s)
assert e == 1
s = np.array([[0, 0, 0], [0, 1, 0], [0, -1, 0]])
e = g.run_a_game(p1, p1, s)
assert e == 1
s = np.array([[0, 0, 0], [0, 1, 0], [-1, 0, 0]])
e = g.run_a_game(p1, p1, s)
assert e == 0
#******************************************************
#*******************(TRY ME)***************************
#******************************************************
'''Run A Complete Game (TicTacToe):
the following code will run a complete TicTacToe game using MiniMaxPlayer,
if you want to try this, uncomment the following three lines of code.
Note: it may take 1 or 2 minutes to run
'''
#g = TicTacToe()
#e = g.run_a_game(p1,p1)
#assert e==0
#******************************************************
#******************************************************
#******************************************************
#----------------------------------------------
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
s = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
for i in range(10):
e = g.run_a_game(p1, p2, s=s, x=1)
assert e == 1
w = 0
for i in range(10):
e = g.run_a_game(p2, p2, s=s, x=1)
w += e
assert np.abs(w) < 9
#******************************************************
#*******************(DO NOT TRY ME:)*******************
#******************************************************
''' Run A Complete Game (Othello):
def test_MCTS_choose_a_move():
'''(10 points) MCTS choose_a_move'''
#---------------------
# Game: TicTacToe
g = TicTacToe() # game
p = MCTSPlayer()
b = np.array([[0, -1, -1], [0, 1, 0], [0, 0, 0]])
s = GameState(b, x=1)
r, c = p.choose_a_move(g, s)
assert r == 0
assert c == 0
b = np.array([[0, 0, -1], [0, 1, -1], [0, 0, 0]])
s = GameState(b, x=1)
r, c = p.choose_a_move(g, s)
assert r == 2
assert c == 2
b = np.array([[0, 0, 1], [0, -1, 1], [0, 0, 0]])
s = GameState(b, x=-1)
r, c = p.choose_a_move(g, s)
assert r == 2
assert c == 2
p1 = MCTSPlayer()
p2 = RandomPlayer()
p3 = MiniMaxPlayer()
'''random vs MCTS'''
for i in range(10):
b = np.array([[0, -1, 1], [-1, 1, -1], [0, -1, -1]])
s = GameState(b, x=1)
e = g.run_a_game(p1, p2, s)
assert e == 1
for i in range(10):
b = np.array([[0, -1, 1], [-1, 1, -1], [-1, 1, 0]])
s = GameState(b, x=1)
e = g.run_a_game(p1, p2, s)
assert e == 0
''' Minimax vs MCTS '''
for i in range(10):
b = np.array([[0, 0, 1], [0, -1, 0], [1, -1, 0]])
s = GameState(b, x=1)
e = g.run_a_game(p1, p3, s)
assert e == 0
w = 0
for i in range(10):
b = np.array([[0, 0, 0], [0, 0, 0], [1, -1, 0]])
s = GameState(b, x=1)
e = g.run_a_game(p1, p3, s)
w += e
assert w > 1
''' MCTS vs MCTS '''
w = 0
for i in range(10):
b = np.array([[0, 0, 0], [1, -1, 0], [0, 0, 0]])
s = GameState(b, x=1)
e = g.run_a_game(p1, p1, s)
w += e
assert np.abs(w) < 5
''' MCTS(n_iter=1) vs MCTS(n_iter=100) '''
pm1 = MCTSPlayer(1)
pm100 = MCTSPlayer(100)
w = 0
for i in range(10):
b = np.array([[0, 0, 0], [0, 0, 0], [1, -1, 0]])
s = GameState(b, x=1)
e = g.run_a_game(pm100, pm1, s)
w += e
assert np.abs(w) > 4
#----------------------------------------------
# The AI agent should be compatible with both games: TicTacToe and Othello.
# now let's test on the game "Othello":
#---------------------
# Game: Othello
g = Othello() # game
b = np.array([[0, -1, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
for i in range(10):
s = GameState(b.copy(), x=1)
e = g.run_a_game(p1, p2, s)
assert e == 1
''' MCTS vs random'''
s = GameState(b, x=1)
e = g.run_a_game(p1, p2, s)
assert e == 1
